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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS 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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Using Random Forest to Classify Financially Eligible Students for UKT Pradhana, Anak Agung Surya; Batubulan, Kadek Suarjuna; Kotama, I Nyoman Darma
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 4 (2023): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.250

Abstract

This research investigates the use of a Random Forest-based classification model to automate the process of determining students' financial eligibility for the Uang Kuliah Tunggal (UKT) tuition assistance system in Indonesia. By leveraging socioeconomic data such as household income, family size, parental education level, and student performance, the model aims to enhance transparency, fairness, and efficiency in financial aid allocation. The dataset, comprising 1,000 student records with categorical and numerical features, was split into training (80%) and testing (20%) sets. The Random Forest model achieved a high overall accuracy of 90%, with exceptional performance for the Worthy class, attaining a recall of 100% and an F1-score of 0.94, ensuring no eligible students were overlooked. However, the model demonstrated lower recall (60%) for the Not worthy class, indicating room for improvement in addressing class imbalance. Key socioeconomic factors emerged as significant determinants, aligning with traditional UKT criteria. Future work should focus on enhancing model performance through data balancing techniques, feature enrichment, and exploring advanced machine learning algorithms. This research underscores the potential of data-driven approaches to improve the equity and efficiency of tuition assistance systems in higher education.
Edukasi Pembuatan Eco Enzyme dari Limbah Kulit Kopi untuk Pemberdayaan Kelompok Tani Alir Coffee Lusiani, Cucuk Evi; Dewajani, Heny; Hadiantoro, Sigit; Chrisnandari, Rosita Dwi; Batubulan, Kadek Suarjuna; Putri, Shabrina Adani
KOMUNITA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 4 No 4 (2025): November
Publisher : PELITA NUSA TENGGARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60004/komunita.v4i4.236

Abstract

Alir Coffee adalah sebuah bisnis yang bergerak dalam pengolahan kopi yang mengelola semua aktivitas pertanian seperti budidaya kopi, pemrosesan, hingga pemasaran. Salah satu kelompok tani Alir Coffee adalah Kelompok Tani Kopi Lereng Arjuna yang terletak di Desa Ketindan, Kecamatan Lawang, Kabupaten Malang. Wilayah Malang yang mencakup Kecamatan Lawang memiliki topografi yang menguntungkan untuk budidaya kopi sehingga cocok untuk mengembangkan bisnis ini. Namun, kelompok tani Lereng Arjuna menghadapi permasalahan tentang manajemen limbah, terutama limbah dari produk samping pengolahan kopi, yaitu kulit kopi. Salah satu alternatif solusi untuk mengatasi permasalahan ini adalah dengan mengolah limbah kulit kopi menjadi eco enzyme, yang memiliki banyak manfaat. Sebagai bentuk Pengabdian pada Masyarakat (PPM), Jurusan Teknik Kimia, Politeknik Negeri Malang (Polinema) mengadakan bimbingan teknis sebagai bentuk edukasi dalam membuat eco enzyme dari limbah kulit kopi untuk kelompok tani yang bermitra dengan Alir Coffee di Desa Ketindan, Kecamatan Lawang. Kegiatan ini sejalan dengan Rencana Strategis Polinema (Renstra Polinema 2021–2025) yang bertujuan untuk menciptakan iklim bisnis bagi usaha mikro, kecil, dan menengah (UMKM) termasuk kelompok tani Alir Coffee. Melalui kegiatan ini, para peserta diharapkan mendapatkan wawasan dan pengetahuan baru tentang pemanfaatan limbah kopi menjadi eco enzyme sebagai solusi terhadap masalah lingkungan serta penguatan kolaborasi dengan pihak eksternal. Dengan cara ini, kelompok petani diharapkan mampu menghadapi tantangan pengelolaan limbah dan mempertahankan keberlanjutan industri kopi di masa depan. Kegiatan PPM dilaksanakan dengan lancar dari Juni hingga Agustus 2024. Para peserta sangat antusias dalam pelajaran praktis tentang pembuatan enzim ramah lingkungan melalui metode fermentasi langsung.
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.
Co-Authors 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, Aniek Suryanti 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 Hapsari, Ratih Indri 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 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 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