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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Scientific Journal of Informatics Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Polinema Kepada Masyarakat Jurnal Pengabdian kepada Masyarakat International Journal of Educational Review, Law And Social Sciences (IJERLAS) JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) International Journal of Health, Engineering and Technology Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Informatika Polinema (JIP)
Batubulan, Kadek Suarjuna
Jurusan Teknologi Informasi, Politeknik Negeri Malang
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Evaluating Service Quality Metrics with AdaBoost Classifier at Restaurant X Batubulan, Kadek Suarjuna; Pratama, I Putu Adi; Naswin, Ahmad
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 3 (2024): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.234

Abstract

This paper explores the use of the AdaBoost classifier to evaluate service quality metrics in the restaurant industry, specifically at Restaurant X. The study focuses on how machine learning, particularly ensemble learning algorithms, can improve the understanding of customer satisfaction by analyzing various service attributes, such as food quality, staff behavior, wait times, and ambiance. By applying AdaBoost, the model combines multiple weak classifiers to create a stronger, more accurate prediction model that identifies key factors influencing customer experience. The research highlights the importance of real-time data and customer feedback in refining service quality metrics and suggests that incorporating sentiment analysis and other dynamic data sources can provide a more comprehensive view of customer satisfaction. The findings suggest that using machine learning algorithms, like AdaBoost, can enhance operational decision-making, improve customer service, and contribute to overall business success. Additionally, the study proposes the continuous updating of the model to reflect changing customer preferences and trends in the competitive food service industry. This approach can lead to better service, customer retention, and a strategic advantage for restaurants seeking to meet the evolving demands of the market.
ARIMA Model for Time Series Forecasting of Doge Coin Prices Batubulan, Kadek Suarjuna; Pratama, I Putu Adi; Naswin, Ahmad
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 1 (2024): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.242

Abstract

The volatility and speculative nature of cryptocurrencies present significant challenges for accurate price forecasting. This study evaluates the performance of the AutoRegressive Integrated Moving Average (ARIMA) model in predicting Dogecoin (DOGE) prices based on historical data obtained from reputable cryptocurrency platforms such as Binance, Coinbase, and CoinGecko. The ARIMA(5,1,0) model demonstrated strong performance under stable market conditions, achieving a Mean Squared Error (MSE) of 0.0006656 and a Root Mean Squared Error (RMSE) of 0.0258, effectively capturing linear price trends. However, the model’s limitations in handling high volatility and non-linear dependencies—common characteristics of cryptocurrency markets—were also identified. To address these challenges, the study explores hybrid ARIMA–neural network models that integrate statistical and machine learning approaches, improving predictive accuracy during periods of market instability. The results suggest that while ARIMA provides a solid baseline for time series forecasting, hybrid and sentiment-aware models incorporating social media and blockchain metrics offer more robust and adaptive solutions for dynamic cryptocurrency markets.
Cataract Classification in Eye Images Using MobileNetV2 Batubulan, Kadek Suarjuna; Pratama, I Putu Adi; Naswin, Ahmad
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 2 (2025): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.268

Abstract

Cataract remains one of the primary causes of visual impairment globally, with early detection being essential to prevent permanent blindness and improve patient quality of life. However, conventional diagnosis depends on ophthalmologists and clinical-grade imaging devices, which are often limited in remote or under-resourced areas. This condition highlights the need for an efficient, accessible, and automated screening solution. To address this challenge, this study utilizes the MobileNetV2 deep learning architecture to classify cataract conditions based on eye images. MobileNetV2 is selected because of its lightweight model structure and strong feature representation capabilities, making it suitable for deployment in portable or embedded medical systems. The dataset used consists of two cataract stages, namely immature and mature cataracts, with images undergoing preprocessing prior to model training. The proposed system demonstrates excellent performance, achieving an accuracy, precision, recall, and F1-score of 100% in distinguishing cataract stages. These results confirm that MobileNetV2 can effectively support cataract screening with high reliability while maintaining efficiency. Future work will involve extending the dataset to include additional cataract severity levels and non-cataract eye images, as well as integrating explainable artificial intelligence methods to provide visual diagnostic interpretations and enhance clinical trust in real-world applications.
Optimalisasi Pembibitan Kentang di Greenhouse dengan Teknologi IoT Berbasis Sensor NPK Ismail, Ade; Al Hadid Firdaus, Vipkas; Suarjuna Batubulan, Kadek; Affandi, Luqman
Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Vol 14 No 1 (2026): Jurnal Ilmiah Rekayasa Pertanian dan Biosistem
Publisher : Fakultas Teknologi Pangan & Agroindustri (Fatepa) Universitas Mataram dan Perhimpunan Teknik Pertanian (PERTETA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jrpb.v14i1.1210

Abstract

This study aims to design and implement an Internet of Things (IoT)-based Smart Precision Farming system to optimize potato seedling cultivation through precise and efficient nutrient management. The system is developed using a dual-node ESP32 microcontroller architecture for real-time microclimate data acquisition and soil NPK level monitoring using an industrial-grade sensor with the RS485 Modbus communication protocol. The automated fertigation strategy is implemented using Mamdani Fuzzy Logic integrated with MQTT, Node-RED, and Grafana platforms for data visualization. Technical performance evaluation reveals high system reliability, with a Mean Absolute Percentage Error (MAPE) of 2.62% for the NPK sensor and actuator response latency of <500 ms. Greenhouse implementation proves that transitioning from schedule-based to demand-based fertilization significantly increases fertilizer efficiency by up to 30.3%. This reduction in fertilizer volume does not trigger nutrient deficiency but instead optimally stimulates the agronomic growth of potato seedlings (Sig. < 0.05), indicated by a 15.9% increase in plant height and a 33.3% increase in the number of leaves. The application of this technology offers a concrete and measurable solution to minimize fertilizer waste while improving growth quality in the horticultural seedling phase.
DEVELOPMENT OF A PRESENCE SYSTEM WITH FACE RECOGNITION INTEGRATED WITH ONLINE APPLICATIONS USING DEEP LEARNING METHODS TO EXPAND THE TEACHING AND LEARNING PROCESS AT SMKN 9 MALANG Fullchis Nurtjahjani; Galih Putra Riatma; Kadek Suarjuna Batubulan; Ane Fany Novitasari
International Journal of Educational Review, Law And Social Sciences (IJERLAS) Vol. 4 No. 5 (2024)
Publisher : CV. RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijerlas.v4i5.1958

Abstract

Attendance systems are needed in various fields such as companies, government agencies, educational agencies, and others. Especially for educational institutions, the attendance system functions to control or determine the presence of students, teaching staff, and educational staff. SMKN 9 Malang City is an educational institution that has the obligation and role to equip its graduates with life skills in an integrative manner, which combines generic and specific potential to solve and overcome life's problems. This school has 5 expertise concentrations from which students can choose. In measuring the presence of the academic community, SMKN 9 Malang City uses an attendance system that includes Finger (for students and PTT and GTT) and the application from the East Java Province BKD e-Presence ASN. However, in its use several weaknesses were found that prevented the application from running efficiently, these weaknesses were 1) the number of locations, 2) a large number of students, 3) Time was less effective because the ratio between tools and students was still not ideal, 4) Sometimes some students have to try several times for less sensitive fingerprints, 5) Not connected with parents so that the school, the students' guardians/committee collaborate in monitoring their son. To follow up on problems with the attendance system used, schools need a more effective and efficient attendance system, namely by using Face Recognition Integrated with Online Applications Using Deep Learning Methods. The system created can make attendance easier for students, teaching staff, and educational staff. It is hoped that this system can improve student discipline and make it easier to monitor the performance of teaching staff and educational staff. Keywords: Face Recognition, Deep Learning, Attendance
INFORMATION SYSTEM FOR THE ADMISSION OF NEW STUDENT CANDIDATES FOR THE BIDIKSIBA SCHOLARSHIP PATHWAY POLYTEKNIK NEGERI MALANG Kadek Suarjuna Batubulan; Ratih Indri Hapsari; Budi Harijanto; Ane Fany Novitasari
International Journal of Educational Review, Law And Social Sciences (IJERLAS) Vol. 5 No. 1 (2025)
Publisher : CV. RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijerlas.v5i1.2441

Abstract

The Bidikiba Program (Education Scholarships Around Bukit Asam) is a program of educational scholarships provided by PT Bukit Asam Tbk (PTBA) to students high school graduates or equivalent from underprivileged families around the operational area companies to be able to continue their education at university. Implementation of Bidikiba scholarship acceptance selection is still done manually registration process, selection exams, and ranking of prospective students. Registration This is done by recording prospective students using Excel and if any Changes or errors in prospective students' data must be reported to the admin make edits to the data. The system is expected to help resolve problems in the process of accepting prospective students for the Bidikiba scholarship programat Malang State Polytechnic.
Co-Authors Ade Ismail Ade Ismail Ade Ismail Adisaksana, Helmi Adisaksana, Helmy Agustina, Hiqma Nur Ahmad Nova Rifki Wildani Aini, Lilies Nur Alifia Zaida Nurmaya Ananta, Ahmadi Yuli Andika Hilmi Hamdani Ane Fany Novitasari Ane Fany Novitasari Ane Fany Novitasari Aniek Suryanti Kusuma Anisa Taufika Firdausi Annisa Taufika Firdausi Annisa Taufika Firdausi Annisa Taufika Firdausi Astiningrum, Mungki Ayu Febriyanti Puspitasari ‘Aini, Yulis Nurul Banni Satria Andoko Budi Harijanto, Budi Chrisnandari, Rosita Dwi Danang Eko Novianto Dewajani, Heny Dian Hanifudin Subhi Dika Rizky Yunianto Dwi Pribadi, Joni Elisiana, Malia Fadloli Fadloli Fadloli, Fadloli Fauziah, Suci Nur Galih Putra Riatma Helmi Adisaksana I Putu Adi Pratama, I Putu Adi Ilham Ibrahim Imam Fahrur Rozi Irsyad Arif Mashudi Joni Dwi Pribadi Kotama, I Nyoman Darma Lilies Nur Aini Luqman Affandi Lusiani, Cucuk Evi Malia Elisiana Millenia Rusbandi Mohammad Maskan Mohammad Maskan, Mohammad Naswin, Ahmad Noprianto, Noprianto Novianto, Danang Eko Nurtjahjani, Fullchis Pradhana, Anak Agung Surya Putra Prima Arhandi, Putra Prima PUTU SUGIARTAWAN Ratih Indri Hapsari Ridwan Rismanto RIZKY ARDIANSYAH Rosa Andrie Asmara Rusbandi, Millenia Sanita Dhakirah Sanita Dhakirah Shabrina Adani Putri, Shabrina Adani Shoumi, Milyun Ni’ma Sigit Hadiantoro, Sigit Suci Nur Fauziah Ulla Delfana Rosiani Vipkas Al Hadid Firdaus Wardani, Ni Wayan Wildani, Ahmad Nova Rifki Yulis Nurul Aini