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All Journal ELINVO (Electronics, Informatics, and Vocational Education) Jurnal IPTEK Infotekmesin Jurnal Teknologi dan Terapan Bisnis Asian Journal of Management, Entrepreneurship and Social Science
Aris Puja Widikda
Politeknik Semen Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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THE KEY PERFORMANCE INDIKATOR KINERJA TEKNOLOGI PENGOLAHAN SAMPAH TPA MENJADI BAHAN BAKAR ALTERNATIF RDF (REFUSE DERRIVED FUELS): KEY PERFORMANCE INDIKATOR KINERJA TEKNOLOGI PENGOLAHAN SAMPAH TPA MENJADI BAHAN BAKAR ALTERNATIF RDF (REFUSE DERRIVED FUELS) Aris Puja Widikda; Farid Mujayyin; Nugrahadi DM
Jurnal Teknologi dan Terapan Bisnis Vol. 5 No. 2 (2022): Vol 5 No 2 (2022): Jurnal Teknologi dan Terapan Bisnis
Publisher : Program Studi Teknologi Informasi

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Abstract

Teknologi ini sangat berpotensi nilai pemanfaatnnya untuk menghasilkan material yang lebih untuk digunakan sebagai sumber bahan bakar dalam keperluan proses produksi di beberapa industri dan juga keperluan kehidupan sehari-hari. Teknologi pengolah sampah menjadi RDF (Refuse Derived Fuels) terdapat beberapa tahapan dari teknologi tersebut diantaranya memilah jenis sampah, mencacah sampah hingga sampai pada pengeringan dan pencetakan sampai potongan sampah menjadi cetakan briket. Tujuan dari penelitian ini untuk menentukan proses produksi optimal diperlukan penentuan KPI (Key Performance Indikator). Standar operasional pengolahan sampah menjadi RDF dimaksimalkan produksinya dengan menggunakan six sigma DMAIC diantaranya menetapkan standart produksi RDF, mengukur kemampuan mesin, menentukan penyebab utama kerusakan mesin, menentukan perbaikan-perbaikan dan meningkatkan laju produksi, serta mengontrol proses produksi. Pada penelitian ini dapat teridentifikasi penyebab mesin rendah disebabkan oleh kendala pada mesin shredder yang sering mati setelah dilakukan perbaikan. Hasil sasaran KPI distribusi RDF ke pabrik 2 kali/hari, Availability ratio 85 %, Performance ratio 88 %, Quality ratio 86 %, Jumlah mekanik 1 orang, meningkatnya inspeksi 2 kali/hari, menurunnya downtime 2 kali/bulan, jumlah perawatan berkala 3 kali, jumlah peralatan tool/sparepart 10 unit, produk cacat 5 kg. Setelah dilakukan perhitungan nilai OEE (Overal Effectiveness Equipment) dapat ditentukan dan dioptimalkan. Berdasarkan nilai OEE mencapai 94% . Sedangkan sasaran KPI (key performance indicator) dengan metode balancing scorcade bahwa teknologi pengolah sampah dinilai dari kapasitas produksi 4 ton/hr.
PENGUKURAN KINERJA DALAM KONVERSI SAMPAH TPA MENJADI SUMBER ENERGI ALTERNATIF: PENGUKURAN KINERJA DALAM KONVERSI SAMPAH TPA MENJADI SUMBER ENERGI ALTERNATIF Aris Puja Widikda; Farid Mujayyin; Nugrahadi DM
Jurnal Teknologi dan Terapan Bisnis Vol. 5 No. 2 (2022): Vol 5 No 2 (2022): Jurnal Teknologi dan Terapan Bisnis
Publisher : Program Studi Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.0301/jttb.v5i2.150

Abstract

Waste to RDF (Waste Derived Fuel) waste processing technology involves several steps, including sorting the waste, grinding the waste until it is dry, and molding the dried waste into waste briquettes. The aim of this research is to determine the optimal production process, so it is necessary to determine KPI (Key Performance Indicator). Waste processing operational standards at RDF are maximized using six sigma DMAICs including establishing RDF production standards, measuring machine capabilities, identifying the main causes of machine failure, determining improvements and accelerating production as well as controlling the production process. In this research, the cause of poor engine performance can be determined to be due to the pressure exerted on the crusher which often stops after being repaired. KPI Target Results Delivery of RDF to the factory twice a day, Availability level 85%, Efficiency level 88%, Quality level 86%, Number of mechanics 1 person, increased inspections twice a day, reduced downtime 2 times/month, number of routine maintenance 3 times , number of tools/spare parts 10 pcs, defective product 5 kg. After carrying out calculations, the OEE (Overall Effective Equipment) value can be determined and optimized. Based on the OEE value it reaches 94%. While the objective KPI (key performance indicator) using the scorcade balance method is waste treatment technology which is evaluated from a production capacity of 4 tons/hour
Application of Repairing Agitator Mixer Tank in Watertreatment Technology as an Effort to Improve the Quality of Processing River Water to Make it Clean Aris Puja Widikda; Farid Mujayyin; Rizkiyah Nur Putri
Asian Journal of Management, Entrepreneurship and Social Science Vol. 4 No. 03 (2024): August Asian Journal of Management Entrepreneurship and Social Science ( AJMES
Publisher : Cita Konsultindo Research Center

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Abstract

Water is a source of our daily lives, both to meet the needs of society and industry. We all agree that there is an opinion that states that water is a necessity that cannot be replaced by other goods. In reality, currently the real conditions are at an alarming stage if not critical, both in terms of water quantity and quality. If this condition is left untreated and not anticipated from now on, it will become a potential conflict and disaster for industrial society in particular. The limited supply of water resources and the challenges of managing water in accordance with sustainability are very necessary. Handling problems in the water resources sector, such as reservoir or dam infrastructure for water sources, river water management, water purification, in the future, if not done immediately, could have an impact on disrupting human activities and welfare, so that in the future it requires comprehensive handling. Problems with river water processing in the water treatment area are based on observations of brownish water color, pH values ​​above 9 mg/l, water quality not in accordance with established standards. Based on interviews with water treatment duty officers, the turbidity of the water is caused by particles suspended in the water which causes the water to look cloudy, dirty, even as if there is mud deposits. According to the procedure for the purification storage tank, chemical mixing has been added to the chemical tank. After being identified in the field, the electric motor driving force as the energy source and the gearbox used to reduce the rotation speed to only 60 (Rpm) experienced a slowdown in stirring in the mixer agitator so that the coagulation process was slow so that the solid particles in the water content formed less than perfect flocs. Method of solving problems in water treatment with efforts to improve component settings with the aim of improving the standard operational performance of water treatment procedures and maintenance procedures such as setting speed and improving the stirring power of the agitator mixer by rotating the impeller which was initially not mixed evenly, it is hoped that the chemicals and water will react perfectly so that the water becomes clear. The results of improvements and settings on the water treatment components show that the water pH level has increased and the quality of water clarity has reached 7.71 NTUs.
IoT-Based Predictive Maintenance for AC Motors in Water Treatment Plants Using Multi-Sensor Data and LSTM Networks with GAN Augmentation Angga Debby Frayudha; Aris Puja Widikda
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 10 No. 2 (2025): November 2025
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v10i2.89410

Abstract

AC motors are critical assets in water treatment plants because they operate continuously to drive key processes. Reactive or schedule-based maintenance can miss early degradation and increase the risk of unplanned downtime. This study presents a field implementation of an Internet of Things (IoT)-based predictive maintenance system in a WTP. The system integrates vibration, temperature, and rotational speed (RPM) sensors with a cloud-based IoT pipeline for real-time data acquisition. Operational data were collected for 30 days from a single motor unit and analyzed using Random Forest and Long Short-Term Memory models. To address limited abnormal-event data, Generative Adversarial Network (GAN)-based augmentation was applied during training. The results show that LSTM performed more consistently than Random Forest; after augmentation, the F1-score improved from 0.92 to 0.95. The monitoring data also captured warning-level changes during operation, including vibration up to 3.9 mm/s, temperature up to 95 °C, and rotational speed dropping to around 1420 RPM, which may indicate abnormal operating conditions requiring inspection. Given the single-unit scope and short duration, the findings are reported as an initial implementation case study. Nevertheless, the work demonstrates the feasibility of a low-cost IoT-based monitoring and prediction framework to support maintenance decisions in WTP operations.
Desain dan Pengujian Predictive Maintenance Agitator Berbasis Internet of Things untuk Sistem Pengolahan Air Aris Puja Widikda; Angga Debby Frayudha
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.3004

Abstract

Agitators play an important role in the mixing, coagulation, and flocculation processes in the water treatment industry. An Internet of Things (IoT)-based predictive maintenance system was designed for early detection of agitator anomalies, including shaft imbalance/misalignment, bearing degradation, slip/RPM drop due to overload, and motor overheating. The system uses ESP32 as an edge device with an SW-420 (vibration pulse) sensor, a DS18B20 (motor temperature) sensor, and a Hall effect sensor (RPM). Data is sent via MQTT to the cloud server for real-time visualization on the dashboard. Validation against the reference instrument showed a MAPE of 0.518% and a correlation of 0.999. Anomaly warnings are triggered when the temperature is 69.5–70°C (critical 72°C) and vibration exceeds the 3σ threshold (warning 410 pulses; critical 550 pulses).
Development of IOT-Based Predictive System for Water Treatment for Monitoring Electric Motor Agitators Aris Puja Widikda; Angga Debby Frayudha
Jurnal IPTEK Vol 29, No 2 (2025): December
Publisher : LPPM Institut Teknologi Adhi Tama Surabaya (ITATS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.iptek.2025.v29i2.8230

Abstract

This research aims to develop an Internet of Things (IoT)-based predictive maintenance system for AC electric motors used in water treatment plants. The primary objective is to reduce unplanned downtime and enhance operational reliability by enabling proactive scheduling of maintenance activities. Research design adopts a research and development approach, beginning with a preliminary study, followed by system design, prototype implementation, data acquisition, and performance validation. The system integrates vibration, temperature, and rotation sensors with an Arduino/ESP32 microcontroller for real-time data collection. Data is transmitted via MQTT protocol to a cloud platform for storage and analysis. Machine learning algorithms, including Random Forest and Long Short-Term Memory (LSTM), are applied to classify equipment condition and detect anomalies. To address the limitation of failure data, Generative Adversarial Networks (GANs) are employed to generate synthetic training data, improving model robustness. Experimental results show that vibration levels reached 3.9 mm/s, temperature rose to 95 °C, and motor speed dropped to 1420 RPM, all of which signaled potential failure before actual breakdown. The LSTM model achieved an F1-score of 0.92, which increased to 0.95 when combined with GAN-based data augmentation, outperforming Random Forest. In conclusion, the proposed system demonstrates that integrating IoT with multi-sensor data and advanced machine learning enables early fault detection in AC motors. This approach offers a cost-effective and scalable solution for predictive maintenance, reducing downtime and extending equipment lifespan in water treatment operations.
Co-Authors Angga Debby Frayudha Farid Mujayyin Farid Mujayyin Nugrahadi DM Rizkiyah Nur Putri