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SENSOR SELECTION COMPARISON BETWEEN FUZZY TOPSIS ALGORITHM AND SIMPLE ADDITIVE WEIGHTING ALGORITHM IN AUTOMATIC INFUSE MONITORING SYSTEM APPLICATION Budiyanto, Setiyo; Hakim, Galang Persada Nurani; Firdausi, Ahmad; I. M, Fajar Rahayu
SINERGI Vol 24, No 3 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2020.3.005

One of the critical equipment to support a patient in the hospital would be an infuse system. One of the main problems with the infuse system was manual monitoring. Few researchers try to build a low cost infuse system using a low-cost sensor and microcontroller. This paper proposes a fuzzy Topsis algorithm and Simple Additive Weighting (SAW) algorithm to choose the best sensor for a low cost to the infuse system, which is one of the Multiple Criteria Decision Making (MCDM) problems. Several simulations using three sensors, such as LDR (photoresistor), phototransistor, and photodiode, are performed. By using these two algorithms, it can be shown that the phototransistor emerges as the best sensor with value 1, even though it has the price six times higher from the LDR sensor and three times higher from the photodiode.

Benchmarking In Microcontroller Development Board Power Consumption For Low Power Iot Wsn Application Galang Persada Nurani Hakim; Muhammad Hafizd Ibnu Hajar; Ahmad Firdausi; Eko Ramadhan
Jurnal Teknologi Elektro Vol 13, No 1 (2022)
Publisher : Electrical Engineering, Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jte.2022.v13i1.005

One of the advantages of Wireless Sensor Network would be its ability to reduce cost of communication system using node to node communication. However Wireless Sensor Network also had a disadvantage which is has limited energy which is include this as low power application. This small energy capacity has limit WSN node capability to operate for a long time. In this paper, we compare power consumption for 3 popular microcontroller development platforms that use for fast development and prototyping Wireless Sensor Network node. The power consumption was including active mode (using most energy) and deep sleep mode (using least energy) operation. From benchmarking we can see that lolin ESP32 as a microcontroller development platform has the most efficient in power consumption which is only 40 mA in active and 0.05 in deep sleep mode, compare with arduino pro mini 8 mA in active and 0.3 mA in deep sleep mode, and wemos D1 mini 74 mA in active and 0.13 mA in deep sleep mode. This low power consumption in deep sleep mode has resulting in longer operational time which is almost 48 Month for lolin ESP32

SENSOR SELECTION COMPARISON BETWEEN FUZZY TOPSIS ALGORITHM AND SIMPLE ADDITIVE WEIGHTING ALGORITHM IN AUTOMATIC INFUSE MONITORING SYSTEM APPLICATION Setiyo Budiyanto; Galang Persada Nurani Hakim; Ahmad Firdausi; Fajar Rahayu I. M
SINERGI Vol 24, No 3 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2020.3.005

One of the critical equipment to support a patient in the hospital would be an infuse system. One of the main problems with the infuse system was manual monitoring. Few researchers try to build a low cost infuse system using a low-cost sensor and microcontroller. This paper proposes a fuzzy Topsis algorithm and Simple Additive Weighting (SAW) algorithm to choose the best sensor for a low cost to the infuse system, which is one of the Multiple Criteria Decision Making (MCDM) problems. Several simulations using three sensors, such as LDR (photoresistor), phototransistor, and photodiode, are performed. By using these two algorithms, it can be shown that the phototransistor emerges as the best sensor with value 1, even though it has the price six times higher from the LDR sensor and three times higher from the photodiode.

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network Galang Persada Nurani Hakim; Diah Septiyana; Iswanto Suwarno
Journal of Robotics and Control (JRC) Vol 3, No 4 (2022): July
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i4.15539

In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept.

Fuzzy Mamdani performance water chiller control optimization using fuzzy adaptive neuro fuzzy inference system assisted Galang Persada Nurani Hakim; Rachmat Muwardi; Mirna Yunita; Diah Septiyana
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1388-1395

Fuzzy Mamdani knows as one of the modern control systems. It was known to have a better performance result when compared to conventional methods. However, because the input of this modern control system sometimes is based on human experience, therefore its performance is sometimes below the conventional one. We propose using the adaptive neuro fuzzy inference system assisted (ANFIS) approach to optimize the fuzzy Mamdani membership function input to overcome this problem. We have tested our hypotheses in water chiller applications based on microcontrollers. Even though it is still behind conventional methods to cool 200 ml water, which is 6 minutes, using fuzzy ANFIS methods, we manage to improve the speed performance in cooling water from 20 minutes to 17 minutes, which is from room temperature to just 24 oC.

Soil Energy Harvester for Batteryless Wireless Sensor Network Node using Redox Method Galang Persada Nurani Hakim; Eko Ramadhan; Diah Septiyana
Jurnal Teknologi Elektro Vol 14, No 1 (2023)
Publisher : Electrical Engineering, Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jte.2023.v14i1.009

The Wireless Sensor Network technologies has great advantage that provide us with cheap solution to deal with telecommunication infrastructure problem that don’t exist in extreme and isolated area. However, the biggest problem exist within wireless sensor network was WSN node limited power. In this paper we try to provide battery less power sources for Wireless Sensor Network Node using Redox method. Using 9 combinations of electrodes circuits, it can provide 6.53 volt and turn on Arduino Mini Pro microcontroller. However, the second it turns on Arduino Mini Pro the voltage drops to 1.73 Volts. Hence this energy harvester can provide power to the Arduino Mini Pro microcontroller with unstable power supply.

Comparison in Quality of service Performance For Wireless Sensor Network Routing between Fuzzy Topsis and SAW Algorithm Muhammad Hafizd Ibnu Hajar; Galang Persada Nurani Hakim; Ahmad Firdausi; Eko Ramadhan
Jurnal Informatika: Jurnal Pengembangan IT Vol 6, No 2 (2021): JPIT, Mei 2021
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v6i2.2530

one of the advantages of Wireless Sensor Network would be its ability to reduce cost of communication system using node to node communication. Because of many things data transfer is Wireless Sensor Network operation sometimes has disturbance. A routing algorithm is a network coding that intends to enhance network performance for better operation with or without any disturbance. Fuzzy TOPSIS and SAW as MCDM algorithm is proposed for routing algorithm in Wireless Sensor Network operation. From our simulation both SAW and Fuzzy Topsis algorithm can be used in network coding (routing) to provide better QOS for Wireless Sensor Network compare with shortest path routing. For delay it perform better at about 2/3 (shortest path routing 50 millisecond, both SAW and Fuzzy Topsis algorithm 33 millisecond), and for packet loss at about 3/4 (shortest path routing 21 bit loss, both SAW and Fuzzy Topsis algorithm 16 bit loss). From our simulation both SAW and Fuzzy Topsis algorithm algorithm has benefit which is lower delay and packet loss but at higher cost which is more hopping for communication channel (shortest path routing 3 hopping, both SAW and Fuzzy Topsis algorithm 5 hopping)

Benchmarking in QoS and Energy Consumption SAW and TOPSIS Algoritm in Low Cost Microcontroller for Wireless Sensor Network Routing Application Hakim, Galang Persada Nurani; Septiyana, Diah; Dani, Akhmad Wahyu; Sirait, Fadli
Jurnal Teknologi Elektro Vol 15, No 2 (2024)
Publisher : Electrical Engineering, Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jte.2024.v15i2.008

The Wireless Sensor Network technologies have provides us with cheap and unique solution to deal with telecommunication infrastructure problem that don’t exist in extreme and isolated area. To guarantee the quality of service of Wireless Sensor Network wireless data transmission, a lot of researchers propose to employ a routing algorithm, such as SAW and fuzzy topsis from MCDM algorithm. A lot of routing algorithm in Wireless Sensor Network was based on these algorithms. In this paper we propose to do simulation and real time energy measurement in order to determine the best MCDM algorithm to be use in Wireless Sensor Network routing. In QoS 3x4 node simulation Both algorithm has provide low delay 31 millisecond and low packet loss 16 bit. This good performance in QoS however has disadvantage which has higher hop quantity. In term of energy consumption SAW has less energy consumption (better) compare with fuzzy topsis for each microcontroller development platforms that we have test. Although it was small but we have difference in energy consumption between SAW and fuzzy topsis, for ESP32 it has difference 39 microJoule, for ESP8266 it has difference 129 microJoule and for ATMEGA328P it has difference 2 microJoule.

ANFIS method to enhance FMEA water operation model of Indonesia drinking water distribution system Septiyana, Diah; Abd. Rahman, Mohamed; Mohamed Ariff, Tasnim Firdaus; Rosarina, Desy; Adesta, Erry Yulian T.; Hakim, Galang Persada Nurani; Sukindar, Nor Aiman
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.016

Many problems are found in water treatment and distribution in water operations. Those problems range from low to critical risk. All critical risks must be addressed immediately. The Failure Mode and Effects Analysis (FMEA) prioritizes problems based on Occurrence, Severity, and Detection values to identify critical risks. However, this method is also having problems. With the same risk priority number (RPN) calculation, FMEA would be a ranking problem with the same RPN value; hence, we have a priority problem that is not critical but based on the highest value. To solve this problem, we propose additional methods, such as the ANFIS, to give weight based on risk level classification. From the results of data processing carried out by the ANFIS method, it is proven that it can perform re-ranking, for example, in the L2, R5, S8, and U3 code, which has an FMEA RPN value of 12. However, in FMEA-ANFIS, the RPN value becomes L2 2.05, R5 1.52, S8 1.32, and U3 2.52. Furthermore, with these results, it can be concluded that the ANFIS method can enhance the FMEA model in water operations.

Perancangan Sistem Routing Pada Jaringan Sensor Nirkabel Menggunakan Algoritma SAW-DIJKSTRA Habibi, Hamzah; Hakim, Galang Persada Nurani
Jurnal Teknologi Elektro Vol 15, No 3 (2024)
Publisher : Electrical Engineering, Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jte.2024.v15i3.007

Penentuan jalur adalah hal yang penting dalam proses transportasi dan pertukaran informasi pada sebuah jaringan WSN (Wireless Sensor Network). Perpindahan data dari node asal menuju node tujuan harus dilakukan dengan mempertimbangkan nilai dari kriteria-kriteria yang ada pada setiap node selama perjalanan, agar data dapat dikirim dengan optimal dan efisien. Tujuan utama dari penelitian ini adalah mengidentifikasi jalur terbaik berdasarkan beberapa kriteria. Metode SAW (Simple Additive Weighting) merupakan algoritma MCDM (Multi Criteria Decision Making) sedangkan Dijkstra merupakan algoritma shortestpath yang hanya dapat mempertimbangkan satu kriteria saja. Untuk mencapai tujuan utama, yaitu, menentukan jalur terbaik, diterapkan algoritma SAW-Dijkstra. Pada penelitian ini, algoritma gabungan SAW-Dijkstra ditinjau dan diperbandingkan dengan algoritma SAW tanpa Dijkstra dalam sebuah skema jaringan dengan nilai kriteria yang sama menggunakan simulasi pada sebuah aplikasi web yang telah dirancang sedemikian rupa. Hasil dari penelitian ini menunjukkan bahwa algoritma SAW-Dijkstra mendapatkan kinerja yang lebih baik, dimana pada jaringan 4x3, SAW memperoleh nilai rata-rata delay dan loss sebesar 51.8 dan 20.5, sedangkan SAW-Dijkstra memperoleh nilai rata-rata sebesar 33.5 dan 13.4, sehingga SAW-Dijkstra lebih optimal 35.33% (delay) dan 34.63% (loss) dibandingkan dengan SAW, begitu pula pada jaringan 5x5, SAW memperoleh nilai rata-rata delay, loss, throughput dan jitter sebesar 76.2, 41.1, 4930.6, dan 104.1 sedangkan SAW-Dijkstra memperoleh nilai rata-rata sebesar 47.8, 22.2, 2836.3 dan 67, sehingga SAW-Dijkstra lebih optimal 37.27% (delay), 45.99% (loss), 42.48% (throughput), dan 35.64% (jitter) dibandingkan dengan SAW. Algoritma SAW-Dijkstra dapat diterapkan pada berbagai macam topologi jaringan dengan berbagai macam skala dan juga dapat diterapkan tidak hanya khusus pada jaringan WSN, melainkan pada jaringan-jaringan yang lainnya.

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