HASIL DAN LUARAN YANG DICAPAI
4 Accu Battery 12 Volt
4.2 Luaran Penelitian
4.4.7 Hasil pembacaan pada sensor voltase
Tabel 4.10 menampilkan nilai voltase yang keluar dari solar panel. Nilai index yang sangat tinggi dari UV menghasilkan nilai voltase yang maksimum yang dihasilkan oleh solar panel (20 Volt).
Tabel 4.10 Hasil pembacaan sensor voltase
Coordinates No
Testing time Voltage
1 08.06 9 2 08.28 12 3 08.59 20 4 09.13 20 5 09.26 20 6 09.43 20 7 10.07 20 8 10.29 20
4.4.8 Performa sistem selama pengujian
Performa sistem selama pengujian adalah sebagai berikut:
1. Sistem berjalan lancar selama 4 jam pengujian. Transmisi data ke aplikasi cloud dapat berjalan lancar.
2. Performa battery sangat bagus. Solar panel dapat melakukan pengisian selama pengujian. 3. Hampir seluruh sensor memerlukan waktu 15 sampai 20 detik sebelum menampilkan hasil pembacaan yang stabil. Sensor kekeruhan perlu di lindungi dengan pelindung khusus, agar bagian kepala dari sensor tidak terkena air.
Foto selama pengujian alat di tampilkan pada Lampiran.
4.2 Luaran Penelitian
Luaran penelitian ditampilkan sebagai berikut:
Tabel 4.1 Luaran yang dicapai
Submitted (IN REVIEW) Accepted 1 Publikasi ilimiah Internasional International Electrical Engineering and Informatics International Journal of
electrical and computer enginering
2 Pemakalah dalam temu ilmiah International
Conference on Electrical Engineering &
Computer Science
3 Produk/model/purwarupa Purwarupa dengan 3
arsitektur berbeda
4 Komitmen kerjasama Sangat baik
5 Keberlanjutan program
penelitian di TPP
BAB 5
Rencana Tahapan Berikutnya
Tahapan kegiatan penelitian tahun ke -2 di tampilkan pada Tabel 5.1 berikut
Tabel 5.1. Tahapan Kegiatan Penelitian Tahun ke -2
No Rencana Kegiatan 1 2 3 4 5 6 7 8 9 10 11 12 1 Pembuatan sistem Pemantauan berbasis website 2 Pemasangan JSN pada Danau Limboto
3 Uji coba JSN dan
sistem berbasis website
4 Evaluasi Sistem
5 Uji coba setelah
evaluasi
5 Publikasi Ilmiah
(Seminar Internasional, Jurnal Internasional, Laporan Akhir)
BAB 6
KESIMPULAN
6.1 Kesimpulan
1. Sistem Pemantauan Danau Limboto dapat dibangun menggunakan arsitektur yang berbeda, yaitu menggunakan Arduino Mega sebagai microcontroller, menggunakan NodeMcu sebagai microcontroller dan menggunakan XBEE sebagai microcontroller
2. Hasil pembacaan sensor pada Danau Limboto, menunjukkan bahwa Danau Limboto masih pada ambang batas yang dapat di terima dan masih layak di gunakan untuk aktivitas rumah tangga dan perikanan.
3. Sistem yang dibangun baru dapat berjalan sampai dengan 4 jam
6.2 Saran
1. Pelindung node disarankan menggunakan bahan yang tidak mudah panas dan ringan 2. Kalibrasi alat disarankan dilakukan setiap sebulan 1 kali
Daftar Pustaka
[1] Indonesia. Indonesian National Institute of Aeronautics and Space, ―Pedoman Pemantauan Perubahan Luas Permukaan
Air Danau Menggunakan Data Satelit Penginderaan Jauh. 2015. [Online].
Available:http://spbn.pusfatja.lapan.go.id/documents/716/download[Accessed: Aug 10, 2018].
[2] Y.Y. Maulana, ―Integrated Real-time water quality monitoring‖, Jurnal Elektronika dan Telekomunikasi., vol.15, no.1, pp. 23-24, June 2013.
[3] Indonesia. Ministry of Environment and Forestry Republic of Indonesia. ―KLHK Pulihkan 15 Danau Prioritas Nasional‖. [Online]. Availble: http://ppid.menlhk.go.id/siaran_pers/browse/608. [Access on 1st August 2018].
[4] Perumal, T., Sulaiman, M. N., & Leong, C. Y. (2015). Internet of Things (IoT) enabled water monitoring system. 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE).
[5] Vijayakumar, R., Ramya, R. The real time monitoring of water quality IoT environment. (2015). IEEE Sponsored 2nd International Conference on Innovations in Information, Embedded and Communication systems (ICIIECS).
[6] Manurung, P., Gaol, J.L., Katarina, F., Ketaren, D. (2015). Kondisi Aktual Danau Toba: Pemantaan Real Time Tinggi Permukaan air dan kajian sustainability Danau Toba. Seminar dan Pameran ―Save Lake Toba‖.
[7] Harijaya., R., Pangaribuan, N., Sinaga. L., Kisno., Sagian, P., (2017). Smart Monitoring Apps for Salvaging Neolissochillus Theinemanni Sumetaranus (Batak Heritage) From Extinction. International Conference on Electrical Conference on Electrical Engineering and Computer Science (ICECOS).
[8] Sulastri, Sulawesty. F., Nomosatriyo, S.(2015). Long Term Monitoring of Water Quality and Phytoplaton Cahnges in Lake Maninjau, West Sumatra, Indonesia. Oseanologi dan Limnologi di Indonesia. , Vol. 41, no.3. pp. 339-353.
[9] lenntech.com. Turbidity Definition. Available: https://www.lenntech.com/turbidity.htm. [Accessed: 5th October 2018]
[10] Comoxvalleyrd.ca. Level of turbidity. Available: http://
www.comoxvalleyrd.ca/projects-initative/past-current-projects/comox-valley-water-treatment-project/turbidity-and-boil. [Accessed: 5th October 2018].
[11] dfrobot.com. Turbidity sensor. Available:
https://www.dfrobot.com/wiki/index.php/Turbidity_sensor_SKU:_SEN0189. [Accessed: 5th October 2018]
[12] Campbellsci.com. Conductivity. Available: https://www.campbellsci.com/conductivity. [Accessed: 5th October 2018]
[13] Depoinovasi.com. Sensor Konduktivitas. Available:
http://www.depoinovasi.com/produk-510-sensor-konduktivitas--tds--kadar-garam.html.
[14] Engineeringtoolbox.com. Oxgen solubility in Fresh Water. Available:
https://www.engineeringtoolbox.com/oxygen-solubility-water-d_841.html. [Accessed: 5th October 2018]
[15] dfrobot.com. Dissolved Oxygen Sensor. Availble:
https://www.dfrobot.com/wiki/index.php/Gravity:_Analog_Dissolved_Oxygen_Sensor_SKU:SEN0237. [Accessed: 5th
October 2018].
[16] fondriest.com. Conductivity, Salinity & Total Dissolved Solids
https://www.fondriest.com/environmental-measurements/parameters/water-quality/conductivity-salinity-tds/. [Accessed: 5th October 2018].
[17] Epa.gov. Secondary Drinking Water Standards: Guidance for Nuisance Chemicals. https://www.epa.gov/dwstandardsregulations/secondary-drinking-water-standards-guidance-nuisance-chemicals. [Accessed: 5th October 2018].
[18] Enr.gov. Dissolved Oxygen. https://www.enr.gov.nt.ca/sites/enr/files/dissolved_oxygen.pdf. [Accessed: 5th October 2018].
[19] arduinolearning.com. Arduino and GUVA –s12d UV Sensor. Available: http:// arduinolearning.com/code/arduino-guva-s12sd-uv-sensor.php. . [Accessed: 5th October 2018].
[20] hendrybench.com. Arduino 25V voltage sensor module user manual. Available.:
http://henrysbench.capnfatz.com/henrys-bench/arduino-voltage-measurements/arduino-25v-voltage-sensor-module-user-manual/. [Accessed: 5th October 2018].
[21] circuitdigest.com. Iot Digital Thermometer using NodeMcu and LM35. Available:
https://circuitdigest.com/microcontroller-projects/iot-digital-thermometer-using-nodemcu-esp12-and-lm35.[ Accessed:
Lampiran Foto Selama Pengujian
Pengujian alat pada titik 0o33’39.344‖
Pengujian Alat pada titik 0o34’36.242
Pengujian alat pada titik 0o35’02.42
Lampiran Jurnal Internasional (IN REVIEW)
The development and real world testing of solar-based IoT of Limboto Lake Monitoring System T.P.Handayani1, , Reza Firsandaya Malik2, Stephan A Hulukati3, Hasim4, Wahri Sunanda5
1Unviersitas Muhammadiyah Gorontalo
2Universitas Sriwijaya
3Universitas Ichsan Gorontalo
4
Universitas Negeri Gorontalo
5Universitas Bangka Belitung [email protected]
Abstract—The deteriorating condition of lakes due to human activities are unavoidable. LimbotoLake, located at
Gorontalo district in Indonesia, is one of the lakes in critical state. As a result, its condition needs to be monitored from time to time. Hence, the aim of this research is to develop IoT-based Limboto Lake monitoring system which is tested in real world condition at Limboto Lake. The system consists of several sensors namely voltage sensor, UV sensor, and ambient temperature sensor used to assess the performace of the solar panel. In order to monitor the condition of the lake, several sensors are used namely water temperature sensor, pH sensor, turbidity sensor, conductivity sensor, total suspended solid and dissolved oxygen sensor. NodeMcu is used to connect these sensors to mobile wifi and send the data to acloud application. The system was tested at 8 coordinates at Limboto Lake.
Keywords: iot, limboto lake, monitoring system
1. Introduction
Indonesia is an archipelago country which not only surrounded by sea, but also rivers and lakes. According to Indonesian National Institute of Aeronautics and Space, Indonesia consist of840 big lakes and 735 small lakes [1]. However, the quality of the lakes are gradually decreasing due to sedimentation, errosion and fish farming [2].There are 15 lakes which are incritical conditions, one of which is Limboto Lake in Gorontalo Province, Sulawesi Island [3]. Currently, there is no real time online monitoring system at Limboto lake. The authority usually monitor the lake condition by taking sample and testing it in the laboratory. Therefore, this study aims to develop an IoT (internet of Things) device that could provide online and real time information about the limnology condition of Limboto Lake.
2. Literature Review
IoT water monitoring systems have been proposed for various use. [4] proposed a prototype of water level monitoring system to detect flood occurrences especially in disaster prone areas. It used ATmega328P board and ultrasonic sensor to detect water level. [5] proposed and IoT water monitoring system which used raspberry PI B+ model which were used can be used as a core controller. It used several sensor namely temperature sensor, turbidity sensor, pH sensor, conductivity sensor, dissolved oxygen sensor.
The implementation of lake monitoring in Indonesia were conducted in several lakes namely Toba Lake [6,7] which cover monitoring for water surface level, turbidity, pH and temperature. [8] developed an web based system to monitor Maninjau Lake which used water level sensor, pH, dissolved oxygen and conductivity.
Based on the literature review above, the research gap was found. NodeMcu has not been used to develop water monitoring systems. Furthermore, existing prototypes were only tested in laboratory and not in the real world. Additionally, there are few systems that used solar panel as the power supply. More importantly, there is currently no monitoring system implemented at Limboto Lake. Therefore, the aim of this research is to develop a solar-based IoT monitoring system for Limboto Lake, which used NodeMcu to deliver data to a corresponding cloud application.
3. The Proposed Sysem
Fig.1 shows the schematic diagram of the system. It can be seen that the all sensors are connected to each NodeMcu. The advantages of using NodeMcu for each sensor are:
Each sensor can be programmed individually, any changes in the code would not disturb any other sensors. Therefore the maintainance would be less complicated.
If there is short circuit in a sensor, it would not shut down the entire system.
Each NodeMcu can be connected directly to mobile Wi-Fi since it has its own Wi-Fi.
Figure 1. Schematic Diagram of The system The system consists of:
1. Solar Panel
2. Solar Charge Controller 1 (SCC) 3. Solar Charge Controller 2 (SCC) 4. Switch 5. Accu battery 1 6. Accu battery 2 7. Mobile Wifi 8. USB Hub 1 9. USB Hub 2
10. Water temperature sensor connected to NodeMcu 11. Turbidity sensor connected to NodeMcu
12. pH sensor connected to NodeMcu
13. Dissolved Oxygen Sensor connected to NodeMcu 14. UV sensor connected to NodeMcu
15. LM35 Temperature sensor connected to NodeMcu 16. Conductivity sensor connected to NodeMcu 17. Voltage sensor connected to NodeMcu
In order to accommodate all sensors, the use of USB hubs were necessary. The USB hubs were then connected to the Solar Charge Controller to receive power supplied by the solar module. The first USB hub accommodate water temperature sensor, turbidity sensor, pH sensor, and dissolved oxygen sensor. The second USB hub accommodate UV sensor, ambient temperature sensor and voltage sensor. The conductivity sensor needed to be connected to the SCC directly. It needs 2A of electrical current, which cannot be provided by the USB. The mobile Wi-Fi were also connected directly to SCC in order to operate in stable condition.
The detailed explanation of each component is described as follows.
A. Solar Panel Module
The solar panel module consisted of one 20 Watt solar panel, 2 Solar Charge Controllers (SCC), 2 batteries and 2 USB hub. The use of 2 USB hubs were very important to supply the power to all the sensors.
B. Dfrobot pH sensor
An analog pin is used to read data from Dfrobot pH sensor.The value is designated as analogValue which is used in equation 1. The voltage value from equation one is then converted to pHvalue using equation 2.
voltage = (analogValue * 5.0 )/1024
pHvalue = (3.5*voltage + 0.5)-5.5 (1) (2)
Where analogValue was obtained by using the analog pin and phValue is the value of the pH.
C. Dallas Temperature Sensor
A digital pin of NodeMcu is used to read data from Dallas Temperature Sensor. The result could be used directly without any further conversion.
D. Turbidity Sensor
Turbidity is the level of transparency of water due to presence of suspended particles [9]. Turbidity sensor were used to measure the transparency of the lake. Turbidity level can be in the range of 0 to 400 NTU [10]. Fresh clean water has turbidity of 0 to 5 NTU. The higher the turbidity level, the higher the suspended solid in the water. This experiment used turbidity sensor SKU: SEN0189 by Dfrobot. Detailed specification of this sensor can be found in [11].
An analog pin is used to read the output of turbidity sensor, which is then converted to a turbidity value by equation 3.
Turbidity = (analogvalue x (5/1024)) (3)
The turbidity sensor has a cap which needs to be protected from water. The presence of water inside the cap could disturb the sensor reading.
Conductivity sensors are used to measure the ability of water to conduct electrical current [12]. The more ion in the solution, the higher the conductivity [12]. More details regarding the sensor can be found in [13]. Based on the sensor datasheet, 1 gr of 100 ml iodized salt solution could have 14 μS to 147 μS (μSiemens) of conductivity.
The sensor could also measure the Total Dissolved Solid in the water. According to the datasheet, 50 gr of non-iodized salt solution could have 436 ppm of TDS [13].
Conductivity and TDS values are obtained using equation (4) and equation (5)
Conductivity = (0.2142*analogValue)+494.93 (4) TDS = (0.3417*analogValue)+281.08 (5)
F. Dfrobot Dissolved oxygen sensor
Data from dissolved oxygen sensor is read by using an analog pin. The dissolved oxygen value is obtained using calibration code provided at Dfrobot website [15].
Possible level of dissolved oxygen in water are in the range of 0 ppm to 14.6 ppm [17]. The higher the number, the higher the dissolved oxygen.For ambient temperature of 25oC with atmospheric pressure, the number of solubility oxygen in fresh water is approximately 8.3 ppm [18].
G. UV sensor
In this expriment, GUVA-S12SD UV sensor was used. The output of this sensor is in millivolt which needs to be converted to UV index. More detail regarding the conversion of millivolt to UV index can be found in [19]. The UV sensor was used to observe the influence of UV radiation to voltage produced by the solar panel. The sensor were connected to an analog pin of the MCU.
uv = (analogVoltage/(1024*3.3)) (6)
H. Voltage Sensor
25V Voltage Sensor Module was used to read the output voltage of the solar panel. Detailed information of this sensor is available in [20]
v = (analogvalue * 5.0) / 1024.0 (7) voltage = v / (7500/(30000+7500)) (8)
I. Ambient temperature sensor
LM35 sensor was used to measure the ambient temperature. The detailed information about LM35 can be seen in [21].
temp = (analogValue/1024.0)*3300 (9)
celcius = (temp/10) (10)
Where temp is the converted temperature value from analog value, and celcius is the temperature in celcius unit.
J. Mobile Wifi
The mobile Wi-Fi that wasused to transmit data to cloud application was Huawei E5673. It used 4G data connection provided by a local provider.
4. Testing Result
The system was tested on the surface of Limboto lake at 8 coordinates on 28 August 2018 from 08.00 to 11.00. Local fishermen boats were used to place the system to the coordinates. The system was switched off before it was brought to other coordinates to prevent short circuit. The map of the testing site is shown in Fig.2. It was dry season, so the boat was only able to go to coordinates with sufficient water depth only. Shallow level of water can cause damage to boats. The result shows that the system ran well and was able to transfer data for each coordinates
Figure 2. The testing locations of the system The coordinates of the testing location are shown in Table 1
Table 1. Coordinates of the system testing
No Coordinates Testing Time 1 N: 0o33’39.344‖ E: 123o00’32.335‖ 08.06 2 N: 0o34’05.321‖ E: 123o00’12.898‖ 08.28 3 N: 0o34’36.242‖ E: 122o59’57.064‖ 08.59 4 N: 0o34’54.213‖ E: 122o59’28.490‖ 09.13 5 N: 0o35’02.242‖ E: 122o58’57.119‖ 09.26 6 N: 0o34’35.883‖ E: 122o58’38.956‖ 09.43 7 N: 0o34’18.669‖ E: 122o59’06.625‖ 10.07 8 N: 0o34’13.431‖ E : 122O59’39.508‖ 10.29
Table 1 shows the coordinates of the testing system. The distance of each point to another was approximately 1 km.
A. Readings from Temperature Sensors
Table 2 shows the temperature from water temperature sensor and the ambient temperature sensor. The result shows that the temperature increased as the time passed. The difference between the water temperature and the ambient temperature was 5oC - 6oC.
Table 2. Result of Temperature Sensors Reading
Coordinates No
Testing time Water Temperature Ambient Temperature
1 08.06 21.12oC 26 OC 2 08.28 21.09oC 27 OC 3 08.59 22.09oC 28 OC 4 09.13 22.25oC 29 OC 5 09.26 22.33oC 30 OC 6 09.43 23.79oC 30 OC
7 10.07 25.03oC 31 OC
8 10.29 26.05oC 31 OC
B. Readings from pH Sensor
Table 3 shows the result of pH sensor reading in each coordinates. The range of the pH werebetween 7 to 7.79, which mean that the lake pH is acceptable.
Table 3. Result of pH Sensors Reading
Coordinates No
Testing time pHValue
1 08.06 7.73 2 08.28 7.13 3 08.59 7.09 4 09.13 7.55 5 09.26 7.63 6 09.43 7.79 7 10.07 7.37 8 10.29 7.48
It can be concluded from the pH reading result that the closer the coordinate to land,for example coordinates 1,5,and 6, the higher the pH level. The lowest pH value was in coordinate 3 and 7.These locations are in the middle of the lake and far away from human activities. This means that locations which are far away from human activities tend to have more neutral pH.
C. Readings from turbidity sensor
Table 4 shows the level of turbidity in the range of 23.76 to 26.55 NTU. This level is considered high since according to WHO (World Health Organization), drinking water should not have turbidity more than 5 NTU. The high turbidity value caused the lake to look unclean and also means that the suspended particles scatter the light, which contributes to low oxygen concentration in the water [10].
Table 4. Result of Turbidity Sensor Reading
Coordinates No Testing time Turbidity Value (NTU)
1 08.06 23.84 2 08.28 24.57 3 08.59 26.55 4 09.13 25.93 5 09.26 24.46 6 09.43 23.83 7 10.07 24.44 8 10.29 23.76
D. Readings from Conductivity Sensor
Conductivity level of fresh water stream that support diverse aquatic life is in the range of 150 to 500 µS/cm [9]. It can be seen that the conductivity level of Limboto Lake is above the upper limit, which will influence the diverse aquatic life. Maximum water TDS level recommended by the US EPA is 500 ppm [10].
Table 6. Result of Conductivity and TDS Sensor Reading
Coordinates No
Testing time Conductivity Value (μS) TDS Sensor (ppm)
1 08.06 534.77 344.64 2 08.28 531.56 339.51 3 08.59 523.85 327.21 4 09.13 527.27 332.68 5 09.26 530.27 337.46 6 09.43 526.63 331.65 7 10.07 531.34 339.17 8 10.29 529.42 336.09
E. Readings from Dissolved Oxygen Sensor
According to [14] healthy water stream needs to have level of dissolved oxygen in the range of 9.5 to 12 ppm. Acceptable range of dissolved oxygen is from 6.5 to 9.5 ppm. Table 5 shows the dissolved oxygen in Limboto Lake. The range is from 5.58 to 6.79 ppm. This range is in the healthy category but not all fish can survive in this condition.
Table 5. Result of Dissolved Oxygen Sensor Reading
Coordinates No
Testing time Dissolved Oxygen (ppm) 1 08.06 5.58 2 08.28 6.65 3 08.59 6.72 4 09.13 6.75 5 09.26 6.93 6 09.43 6.79 7 10.07 6.33 8 10.29 6.76
F. Readings from UV Sensor
Table 6 shows the UV index on the surface of Limboto Lake. It shows that the index increased as the time passed. The UV index on the surface of Limboto Lake was very high that it reached 10.
Table 6. Result of UV Sensor
Coordinates No
Testing time UV voltage (millivolt) UV Index
1 08.06 889 2 2 08.28 924 6 3 08.59 1014 8 4 09.13 1024 10 5 09.26 1024 10 6 09.43 1024 10 7 10.07 1024 10 8 10.29 1024 10
G. Reading from Voltage Sensor
Table 7 shows the voltage output of the solar panel. Due to high index of sun UVas shown in Table 6, the maximum of voltage of solar panel (20 volt) was reached.
Table 7. Result of Voltage Sensor
Coordinates No
Testing time Voltage
1 08.06 9 2 08.28 12 3 08.59 20 4 09.13 20 5 09.26 20 6 09.43 20 7 10.07 20 8 10.29 20
Performance of the system during the testing The overall system
The system ran well during 4 hours of testing. It could send sensor data without any problem to the cloud application.
Battery
During 4 hours of testing on the lake surface.The panel was able to charge the battery continuously during testing due to high sun radiation.
The sensors
Most of the sensors need 15 to 20 seconds before they show stable result. Turbidity sensors needs to be protected by a waterproof casing to protect the cap from the water. The connection cable of the conductivity sensors were also protected from water by using candle stick glue.
4. Conclusion
This study developed a solar based IoTfor monitoring Limnology condition of Limboto Lake. The system used water temperature sensor, ambient temperature sensor, ph sensor, turbidity sensor, conductivity sensor and dissolved oxygen sensor. UV sensor and voltage sensor were also used to monitor the performance of the solar panel. The system ran well at 8 coordinates of testing, each sensors needs 20 seconds before it shows stable reading.
5. Acknowledgment
High appreciation to Ministry of research and higher education (RISTEK DIKTI) of Indonesian Government in funding this research and Publication. We also acknowledge CDSR (Centre of development of sustainable region) SHERA (Sustainable Higher Education Research Alliance) in funding our publication.
6. References
[22] Indonesia. Indonesian National Institute of Aeronautics and Space, ―Pedoman Pemantauan Perubahan Luas Permukaan Air Danau Menggunakan Data Satelit Penginderaan Jauh. 2015. [Online]. Available:http://spbn.pusfatja.lapan.go.id/documents/716/download[Accessed: Aug 10, 2018].
[23] Y.Y. Maulana, ―Integrated Real-time water quality monitoring‖, Jurnal Elektronika dan Telekomunikasi., vol.15, no.1, pp. 23-24, June 2013.
[24] Indonesia. Ministry of Environment and Forestry Republic of Indonesia. ―KLHK Pulihkan 15 Danau Prioritas Nasional‖. [Online]. Availble: http://ppid.menlhk.go.id/siaran_pers/browse/608. [Access on 1st
August 2018].
[25] Perumal, T., Sulaiman, M. N., & Leong, C. Y. (2015). Internet of Things (IoT) enabled water monitoring system. 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE).
[26] Vijayakumar, R., Ramya, R. The real time monitoring of water quality IoT environment. (2015). IEEE Sponsored 2nd International Conference on Innovations in Information, Embedded and Communication systems (ICIIECS).
[27] Manurung, P., Gaol, J.L., Katarina, F., Ketaren, D. (2015). Kondisi Aktual Danau Toba: Pemantaan Real Time Tinggi Permukaan air dan kajian sustainability Danau Toba. Seminar dan Pameran ―Save Lake Toba‖.
[28] Harijaya., R., Pangaribuan, N., Sinaga. L., Kisno., Sagian, P., (2017). Smart Monitoring Apps for Salvaging Neolissochillus Theinemanni Sumetaranus (Batak Heritage) From Extinction. International Conference on