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All Journal Journal Technology and Implementation Bussines Jurnal Teknologi dan Terapan Bisnis Complete
Anifatul Faricha
Institut Teknologi Sepuluh Nopember
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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ANALISA STUDI TENTANG PERANCANGAN ALAT MONITORING KUALITAS AIR PDAM BERBASIS INTERNET OF THINGS Faricha, Anifatul
Jurnal Teknologi dan Terapan Bisnis Vol 2 No 1 (2019): March
Publisher : AKADEMI KOMUNITAS SEMEN INDONESIA GRESIK

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Di era perkembangan industri yang semakin maju umumnya juga dibarengi dengan meningkatnya produksi limbah pabrik yang mengakibatkan pencemaran dan turunnya kualitas air jika tidak dikelola dengan benar, untuk itu pengawasan air di beberapa titik lokasi yang berbeda perlu dilakukan untuk menjaga kualitas air yang didistribusikan kepada penduduk, terutama air PDAM. Terbatasnya sumber air dibandingkan dengan peningkatan jumlah populasi penduduk di Indonesia dan juga infrakstruktur distribusi air yang sudah tua merupakan tantangan besar dalam monitoring kualitas air secara real time. Maka dari itu, penelitian ini menyajikan analisa studi tentang perancangan alat monitoring kualitas air PDAM berbasis Internet of Things (IoT) yang meliputi pemilihan parameter-parameter yang digunakan dalam menentukan kualitas air, pemilihan sensor-sensor yang sesuai, serta pemilihan platform IoT yang digunakan. Pengawasan kualitas air PDAM akan dilakukan di beberapa sebaran titik di Surabaya menggunakan sensor suhu, sensor kekeruhan, sensor konduktivitas, dan sensor gas oksigen yang terintegrasi sebagai sensor array. Data-data yang berasal dari sensor-sensor tersebut kemudian ditransmisikan ke mikrokontroler yang memiliki modul IoT sehingga pengaksesan informasi dari sentral ke pengguna bisa dimonitor dari mana saja dan kapan saja.
ANALISA STUDI TENTANG PERANCANGAN ALAT MONITORING KUALITAS AIR PDAM BERBASIS INTERNET OF THINGS : ANALYSIS STUDY: DESIGN OF LOCAL WATER SUPPLY QUALITY MONITORING USING INTERNET OF THINGS Faricha, Anifatul; Adiputra, Dimas; Hafidz, Isa; Khaula Amifia, Lora; Riansyah, Moch. Iskandar
Jurnal Teknologi dan Terapan Bisnis Vol. 2 No. 1 (2019): Vol 2 No 1 (2019): Jurnal Teknologi dan Terapan Bisnis
Publisher : UPPM Akademi Komunitas Semen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.0301/jttb.v2i1.61

Abstract

Abstract Nowadays, as the gain of the global manufacturing technology, particularly at the industrial revolution 4.0, commonly it has also been increasing the amount of the waste products which directly impact and contaminate to the water quality. Hence, the water monitoring is required to maintain water’s quality whether it is safe or not, moreover at the local water supply utility (PDAM) which generally consumed by residents. Water quality monitoring becomes a vital issue and main concerns in these current days because the numbers of water resources are limited, whereas the total citizens in Indonesia are continuously increasing. Therefore, this study presents a review analysis about the design of water quality monitoring using the Internet of Things (IoT), which includes the key parameters selection at the water quality, proper sensors selection, and also IoT's platforms selection. Water quality monitoring will be acquired at several points in Surabaya using sensor array contain many sensors such as temperature sensors, turbidity sensors, conductivity sensors, and oxygen gas sensors. Then, data acquired from sensors are transmitted to the microcontroller which has the IoT module. Hence, information access from central to the user can be monitored, downloaded, or controlled everywhere and anywhere.
ANALISA STUDI TENTANG PERANCANGAN ALAT MONITORING KUALITAS AIR PDAM BERBASIS INTERNET OF THINGS : ANALYSIS STUDY: DESIGN OF LOCAL WATER SUPPLY QUALITY MONITORING USING INTERNET OF THINGS Faricha, Anifatul; Adiputra, Dimas; Hafidz, Isa; Khaula Amifia, Lora; Riansyah, Moch. Iskandar
Jurnal Teknologi dan Terapan Bisnis Vol. 2 No. 1 (2019): Vol 2 No 1 (2019): Jurnal Teknologi dan Terapan Bisnis
Publisher : UPPM Akademi Komunitas Semen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.0301/jttb.v2i1.61

Abstract

Abstract Nowadays, as the gain of the global manufacturing technology, particularly at the industrial revolution 4.0, commonly it has also been increasing the amount of the waste products which directly impact and contaminate to the water quality. Hence, the water monitoring is required to maintain water’s quality whether it is safe or not, moreover at the local water supply utility (PDAM) which generally consumed by residents. Water quality monitoring becomes a vital issue and main concerns in these current days because the numbers of water resources are limited, whereas the total citizens in Indonesia are continuously increasing. Therefore, this study presents a review analysis about the design of water quality monitoring using the Internet of Things (IoT), which includes the key parameters selection at the water quality, proper sensors selection, and also IoT's platforms selection. Water quality monitoring will be acquired at several points in Surabaya using sensor array contain many sensors such as temperature sensors, turbidity sensors, conductivity sensors, and oxygen gas sensors. Then, data acquired from sensors are transmitted to the microcontroller which has the IoT module. Hence, information access from central to the user can be monitored, downloaded, or controlled everywhere and anywhere.
DESAIN DETEKSI KESALAHAN BATTERY MANAGEMENT SYSTEM MENGGUNAKAN ALGORITMA KALMAN FILTER PADA MOBIL LISTRIK NASIONAL Khaula Amifia, Lora; Iskandar Riansyah, Moch.; Hafidz, Isa; Adiputra, Dimas; Faricha, Anifatul
Jurnal Teknologi dan Terapan Bisnis Vol. 2 No. 1 (2019): Vol 2 No 1 (2019): Jurnal Teknologi dan Terapan Bisnis
Publisher : UPPM Akademi Komunitas Semen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.0301/jttb.v2i1.63

Abstract

Electric cars are currently being developed by many people because of low pollution and many countries used them in their daily activity. One of the important and main component is a battery, especially the Battery Management System (BMS) which can optimize the implementation of electric cars. BMS can protect and maintain the battery performance efficiently and at the same time can be a fault detection. Basically, It has three important parameters, there are current, voltage, and temperature that must be maintained and there is no overcurrent, overcharging, and discharging for too long because it can cause a fire. The protection of the BMS on electric cars need battery testing and done by taking current and voltage data, which prioritizes discharging and overdischarging test with a capacity of 2,2 Ah or a maximum capacity of 4,2 Volt. This research optimizes the work of BMS when experiencing faults/errors in order to work properly. The battery is modelled with a simple battery model (Rint) which previously identified parameters and formed a state space that aims to make fault detection. The results showed that fault detection using the Kalman Filter algorithm is very efficient and reliable in improving readings of overcurrent and overdischarge data so as to maintain security and extend/lifetime battery so that it can be implemented safely by the public
The Comparative Study for Predicting Disease Outbreak faricha, anifatul; M. Achirul Nanda; Siti Maghfirotul Ulyah; Ni'matut Tamimah; Enny Indasyah; Robin Addwiyansyah Alvaro Samrat
Journal of Computer, Electronic, and Telecommunication Vol. 1 No. 1 (2020): July
Publisher : Institut Teknologi Telkom Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/complete.v1i1.48

Abstract

To know the prediction of disease outbreak, proper predictive modeling is required to represent the dataset. This study presents the comparative predictive modeling for predicting disease outbreak using two models i.e., optimizable support vector machine (SVM) and optimizable gaussian process regression (GPR). The dataset used in this study contains three cases i.e., positive cases, recovered cases, and death cases. The dataset at each case is divided into training dataset for the training process and external validation dataset for the validation process. Based on the training process and validation process, the root mean square error (RMSE) at positive cases, recovered cases, and death cases using optimizable GPR is substantially more effective for prediction than the optimizable SVM. According to the result performance, by applying optimizable GPR, the training process has the average RMSE of 19.54 and the validation process has the average RMSE of 15.85.
DESAIN DETEKSI KESALAHAN BATTERY MANAGEMENT SYSTEM MENGGUNAKAN ALGORITMA KALMAN FILTER PADA MOBIL LISTRIK NASIONAL Khaula Amifia, Lora; Iskandar Riansyah, Moch.; Hafidz, Isa; Adiputra, Dimas; Faricha, Anifatul
Jurnal Teknologi dan Terapan Bisnis Vol. 2 No. 1 (2019): Vol 2 No 1 (2019): Jurnal Teknologi dan Terapan Bisnis
Publisher : UPPM Akademi Komunitas Semen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.0301/jttb.v2i1.63

Abstract

Electric cars are currently being developed by many people because of low pollution and many countries used them in their daily activity. One of the important and main component is a battery, especially the Battery Management System (BMS) which can optimize the implementation of electric cars. BMS can protect and maintain the battery performance efficiently and at the same time can be a fault detection. Basically, It has three important parameters, there are current, voltage, and temperature that must be maintained and there is no overcurrent, overcharging, and discharging for too long because it can cause a fire. The protection of the BMS on electric cars need battery testing and done by taking current and voltage data, which prioritizes discharging and overdischarging test with a capacity of 2,2 Ah or a maximum capacity of 4,2 Volt. This research optimizes the work of BMS when experiencing faults/errors in order to work properly. The battery is modelled with a simple battery model (Rint) which previously identified parameters and formed a state space that aims to make fault detection. The results showed that fault detection using the Kalman Filter algorithm is very efficient and reliable in improving readings of overcurrent and overdischarge data so as to maintain security and extend/lifetime battery so that it can be implemented safely by the public
Co-Authors Adiputra, Dimas Enny Indasyah Hafidz, Isa Iskandar Riansyah, Moch. Khaula Amifia, Lora M. Achirul Nanda Ni'matut Tamimah Riansyah, Moch. Iskandar Robin Addwiyansyah Alvaro Samrat Siti Maghfirotul Ulyah