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All Journal International Journal of Electrical and Computer Engineering TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Pro Bisnis Telematika JUITA : Jurnal Informatika Seminar Nasional Informatika (SEMNASIF) MIX : Jurnal Ilmiah Manajemen Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Matrix : Jurnal Manajemen Teknologi dan Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Journal of Information Systems and Informatics Jambura Journal of Electrical and Electronics Engineering Journal of Innovation Information Technology and Application (JINITA) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Algoritma Jurnal Pengabdian Mitra Masyarakat (JPMM) CSRID Journal of Educational and Learning Studies
Berlilana Berlilana
Universitas Amikom Purwokerto
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Modeling EMIS Adoption with PLS-SEM: Integrating the Government Adoption Model and DeLone–McLean IS Success Model Mardiyanto Mardiyanto; Berlilana Berlilana; Purwadi Purwadi
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1445

Abstract

This study explores the key factors influencing the adoption of the Education Management Information System (EMIS) within Indonesia's Ministry of Religious Affairs (Kemenag), which is vital for managing data and distributing Teacher Professional Allowances (TPG). Data inconsistencies have been a significant challenge, leading to delays in TPG disbursement. To understand the determinants of EMIS adoption, this study integrates the Government Adoption Model (GAM) and DeLone & McLean’s (D&M) Information Systems Success Model. A quantitative approach was used, collecting data from 328 valid responses from MTsN teachers in Kebumen Regency, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that Perceived Uncertainty (PU), Perceived Security (PSC), and Perceived Privacy (PP) positively contribute to Perceived Trust (PT). Additionally, Information Quality (IQ) emerged as the strongest predictor of EMIS adoption, followed by System Quality (SYQ), Service Responsiveness (PSR), and Trust. The study emphasizes that improving data accuracy (IQ), ensuring system reliability (SYQ), and strengthening security measures (PSC) are critical for accelerating EMIS adoption. The findings offer practical implications for Kemenag to optimize the implementation of EMIS, ultimately improving the efficiency and timeliness of TPG disbursements for educators.
A Comprehensive Evaluation of CatBoost and LightGBM Algorithms for Honorarium Prediction on Categorical Datasets with Class Imbalance Slamet Widodo; Fandy Setyo Utomo; Berlilana
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.27363

Abstract

Determining income, including honoraria in the academic environment, is often done manually and subjectively, necessitating a predictive model to objectively determine the honorarium amount. However, the development of the prediction model faces challenges due to the dataset's characteristics, which include categorical data and an imbalanced class distribution. This research aims to evaluate the predictive performance and computational resource efficiency of the CatBoost and LightGBM algorithms in predicting honorariums. The dataset used includes 58,332 actual honorarium data of employees from higher education institution "A" in Purwokerto for the period from January 2024 to February 2025. The methods used include data preprocessing, dataset splitting using Stratified Splitting, modeling with CatBoost, LightGBM, Random Forest, Neural Network, and Linear Regression, as well as evaluation using MSE, RMSE, MAE, R² metrics, and computational resources (execution time, memory, CPU time). LightGBM achieved an RMSE of 665.960 and an R² of 0.54, while recording the lowest memory usage at only 2.67 MB. CatBoost produced an RMSE of 667.395 and an R² of 0.53, excelling in processing categorical features without one-hot encoding. Meanwhile, Linear Regression showed the lowest accuracy and high memory usage. These results confirm that LightGBM is the most optimal choice for fast, efficient, and accurate honorarium predictions. However, this research is limited to testing in a laboratory environment. Further research is recommended to implement direct integration with an active database and the integration of information retrieval methods to enhance the effectiveness and security of real-time honorarium predictions, as well as to integrate interpretability methods such as SHAP to improve decision-making transparency.
Literature Review: Comparison of Machine Learning Algorithms for Sentiment Analysis of Free Nutritious Meals Mukhlisin Mukhlisin; Berlilana Berlilana; Rujiyanto Eko Saputro
SISTEMASI Vol 15, No 3 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i3.6201

Abstract

The Free Nutritious Meal (FNM) program has triggered massive public responses on social media, driving numerous machine learning–based sentiment analysis studies. However, there has been no comprehensive review comparing the effectiveness of these methods. This study adopts a Systematic Literature Review (SLR) approach on 18 studies (2024–2026) to evaluate the performance of computational algorithms and map trends in public sentiment. The main contribution of this research is to provide an empirical guide for selecting Indonesian-language text classification models, while also offering insights into shifts in public perception. Key findings indicate that Support Vector Machine (SVM) is the most frequently used method, whereas the highest accuracy (97%) was achieved by a combination of Logistic Regression, SVM, and Random Forest on large datasets. Temporally, sentiment trends shifted from budget skepticism (2024) to positive acceptance during program implementation (2025–2026). The study’s implications support policymakers in evaluating program effectiveness in real time. The scope and limitations of this research focus on literature within a specific timeframe, with performance evaluation emphasizing quantitative accuracy metrics.
Prediksi Harga Cryptocurrency Multi-Aset Menggunakan Machine Learning dan Deep Learning Yusuf Nur Alam; Berlilana
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3473

Abstract

Cryptocurrency price volatility requires predictive models capable of accurately capturing non-linear patterns. This study predicts the price of Bitcoin (BTCUSDT) as the main asset, as well as Ethereum (ETHUSDT) and Ripple (XRPUSDT) as comparison assets, using Decision Tree, Random Forest, XGBoost, and LSTM models. The novelty of this study lies in the analysis of temporal data leakage and the evaluation of model extrapolation capability within a uniform experimental framework. Daily historical data were processed through cleaning, correlation analysis, variable selection, and sequential 70:30 data splitting. The prediction target was defined as the next-day closing price to avoid data leakage, and the models were evaluated using time-series cross-validation with RMSE, MAPE, and R² metrics. The results show that the best-performing model differs for each asset: LSTM outperformed other models for BTC and XRP, while Random Forest performed best for ETH, with R² values ranging from 0.60 to 0.98. Tree-based models tended to produce flat predictions when test prices exceeded the training data range. These findings emphasize the importance of defining prediction targets, applying temporal validation, and conducting cross-asset evaluation in selecting appropriate models for cryptocurrency price prediction.
Development of interactive learning media through 2D animated video in Indonesian language learning Zhafran Afif Nurdiyansah; Berlilana Berlilana
Journal of Educational and Learning Studies Vol 8, No 2 (2025): Journal of Educational and Learning Studies
Publisher : Global Econedu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32698/02282

Abstract

The development and advancement of technology play a crucial role in various fields, including education. Learning media holds significant importance in the teaching and learning process. Furthermore, the use of learning media can also have positive psychological effects on students. As we know, first-grade students are still at a stage where it can be challenging for teachers to provide explanations that they can fully comprehend. One example is at MI Muhammadiyah Kalilandak, where many students still struggle with reading, especially in the first grade. Since fluent reading is essential, the use of learning media that can support these students is necessary, such as through 2D animated videos. With the inclusion of videos equipped with animation, text, and music, it is hoped that the students will be more motivated in their learning. Therefore, this research will focus on the creation of 2D animated videos to enhance the reading abilities of the students.
Mapping UI/UX Evaluation Methods, Evaluation Objects, and Measured Aspects in Comparative Studies: A Systematic Literature Review Bachtiar Mujaddidi; Berlilana Berlilana; Purwadi Purwadi
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.39319

Abstract

— UI/UX evaluation is a crucial aspect in ensuring the quality of interaction between users and systems, particularly in the context of increasingly complex digital applications. However, the wide variety of available evaluation methods often leads to differences in results and interpretations when assessing system performance and user satisfaction. This study aims to review and compare UI/UX evaluation approaches used in comparative studies. Unlike previous review studies that mainly focused on specific application domains, user perceptions, or technological trends, this study specifically maps the relationship between UI/UX evaluation methods, evaluated objects, and user experience dimensions measured in comparative studies. The method employed is a systematic literature review (SLR) following the PRISMA guidelines. Literature was retrieved from the Scopus database, and through the selection process, 13 articles met the inclusion criteria. The analysis revealed that UI/UX evaluation approaches in comparative studies are predominantly focused on comparing interaction techniques or interaction approaches, accounting for 61.5% (8 articles) of the selected studies. This is followed by comparisons of software performance, which represent 23.1% (3 articles), and comparisons of evaluation methods, which account for 15.4% (2 articles). Evaluations generally employ a combination of performance metrics, user perception measures, and additional experiential indicators such as cognitive workload and physiological responses. The findings show that each method or system demonstrates strengths in specific evaluation dimensions, highlighting the need for a multidimensional and multi-method evaluation approach to obtain a more comprehensive understanding of UI/UX. The main contribution of this study is the development of a systematic synthesis that links evaluation methods, evaluation objects, and UI/UX indicators employed in comparative studies. The findings provide a broader understanding of current evaluation practices and may serve as a reference for researchers and practitioners in designing more appropriate and consistent UI/UX evaluation strategies.
Utilitarian vs Human-Centered AI Acceptance: Explaining Students’ Adoption of ChatGPT in Higher Education Dwi Angesti Dinda Parameswara; Berlilana Berlilana; Rujianto Eko Saputro
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34218

Abstract

The growing use of generative artificial intelligence (AI) in higher education raises questions about how students assess and adopt these systems, particularly whether traditional utilitarian models are sufficient to explain their use. This study compares the Technology Acceptance Model (TAM) and the Human-Centered AI Acceptance Model (HCAIAM) in explaining students’ behavioral intention to use ChatGPT, while examining how functional and human-centered factors operate within the same framework. A cross-sectional design was used, involving 100 undergraduate students in Indonesia selected through convenience sampling, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that TAM provides stronger explanatory power and better model fit (R² = 0.765; SRMR = 0.073) than HCAIAM (R² = 0.709; SRMR = 0.136). Perceived usefulness and perceived ease of use emerge as the main drivers of intention, indicating that students tend to use ChatGPT primarily as a tool to support academic tasks. In contrast, human-centered factors such as transparency and ethical alignment influence intention indirectly through trust and attitude. The autonomy construct shows weak reliability and overlaps with other variables, suggesting limitations in its measurement. These findings indicate that utilitarian factors remain central in this context, while human-centered aspects play a more conditional role, and point to a layered pattern of AI acceptance in which different types of factors operate at different levels.
Analisis Kesuksesan Aplikasi Food Delivery Menggunakan Model DeLone & McLean: Studi Empiris Pengguna ShopeeFood Erina Setyawati; Berlilana; Dhanar Intan Surya Saputra
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3425

Abstract

The digital transformation in the food delivery industry is driving service providers to offer high-quality information systems while ensuring user security and privacy so that satisfaction levels can be maintained. This study was conducted to examine the influence of Information System Quality and Security & Privacy on User Satisfaction among ShopeeFood app users by adopting the DeLone and McLean information system success model and using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) method. The study employed a quantitative approach by collecting data through a questionnaire distributed to 100 ShopeeFood users. The analysis results show that Information System Quality and Security & Privacy have a positive and significant influence on User Satisfaction. An R-squared value of 0.642 indicates that these two variables account for 64.2% of the variation in user satisfaction. These findings confirm that optimal information system quality, supported by adequate security and privacy protections, plays a crucial role in enhancing ShopeeFood user satisfaction. From a theoretical perspective, this study reinforces the relevance of the DeLone and McLean model in the context of digital food delivery services, while from a practical standpoint, the research findings can serve as a reference for service providers to improve system quality, service security, and the overall user experience.
Pengelompokan Tipe Pemain Indonesian Basketball League Berbasis Pca dan Algoritma Clustering Junindra Yuga Pamungkas; Berlilana
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3462

Abstract

The professional basketball industry, including the Indonesian Basketball League (IBL), is increasingly adopting data-driven analytics to support strategic decision-making. However, previous studies on domestic basketball leagues have largely relied on individual performance statistics without applying dimensionality reduction techniques, making traditional player position classifications insufficient for objectively representing the characteristics of modern basketball players. To address this limitation, this study integrates Principal Component Analysis (PCA) with clustering algorithms to analyze 160 IBL players from the 2025 season who met the inclusion criteria (GP >= 5 and MP>= 8 minutes) from a total of 246 registered players. Seven statistical variables were used as input features, including Points Per Game (PPG), Rebounds Per Game (RPG), Assists Per Game (APG), Steals Per Game (SPG), Blocks Per Game (BPG), Field Goal Percentage (FGpercen), and Three-Point Field Goal Percentage (3Ppercen). Principal Component Analysis (PCA) was applied prior to clustering to address multicollinearity among variables and reduce data dimensionality while preserving the essential information contained in the original dataset. The PCA results indicate that two principal components explained seventy-one point six percent of the total data variance. The first principal component (PC1) accounted for forty-eight point six percent of the variance and was primarily influenced by PPG and RPG, whereas the second principal component (PC2) explained twenty-three point zero percent of the variance and was dominated by APG, BPG, and 3P percen. Based on internal validation metrics using the optimal number of clusters (k = 4), four distinct player archetypes were identified: All-Around, Bigman, Role Player, and Three-Point Shooter. The K-Means algorithm produced more compact clusters than Ward's Hierarchical Clustering, achieving a Silhouette Score of 0.25, a Davies–Bouldin Index of 1.36, and a Calinski–Harabasz Index of 74.8, with an agreement rate of 81.9 percent between the two clustering algorithms. The moderate Silhouette Score suggests that the boundaries between player archetypes are gradual rather than sharply defined. This study contributes to the academic literature by proposing a player classification framework that combines dimensionality reduction with comparative clustering validation. The proposed approach provides an empirical basis for professional basketball organizations to support player performance evaluation, talent identification, and recruitment decision-making.
HYBRID DEEP NEURAL NETWORK WITH ATTENTION FOR MONTHLY GOLD PRICE FORECASTING Oktavia Mulyo Nurdiyanti; Giat Karyono; Berlilana Berlilana
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 2 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i2.8219

Abstract

Fluktuasi harga emas yang tinggi dari waktu ke waktu mendorong perlunya pengembangan model prediksi yang andal untuk membantu investor dalam pengambilan keputusan. Penelitian ini bertujuan untuk membangun model prediksi harga emas bulanan menggunakan pendekatan hybrid deep learning yang meng-gabungkan Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), dan Attention Mechanism. Dataset yang digunakan adalah data historis harga emas periode 2013–2023 yang telah diproses menjadi data bulanan dan dinormal-isasi. Proses pelatihan dilakukan dengan konfigurasi hyperparame-ter optimal hasil grid search, yaitu learning rate 0.001, batch size 16, dan epoch 3000. Model dievaluasi menggunakan metrik MAE, RMSE, MAPE, akurasi, dan koefisien determinasi. Hasil evaluasi menunjukkan bahwa model mencapai RMSE sebesar 53.78, MAE sebesar 39.11, MAPE sebesar 2.78%, akurasi prediksi sebesar 97.22%, dan R² sebesar 0.9602. Model mampu mengikuti tren fluktuasi harga emas bulanan dengan tingkat ketepatan yang tinggi. Integrasi CNN, BiLSTM, dan Attention terbukti meningkatkan kinerja prediktif dibandingkan pendekatan konvensional. Dengan demikian, model ini berpotensi menjadi alat bantu yang efektif dalam meramalkan harga emas dan mendukung pengambilan keputusan investasi berbasis data
Co-Authors Agnis Nur Afa Zumaroh Alif Yahya Syafaat Andi Dwi Riyanto Anggi Zahriyatun Anisa Antonius Agung Hartono Ayuni, Ratih Azhari Shouni Barkah Bachtiar Mujaddidi Bagaskoro, Galih Bagus Adhi Kusuma Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Dhanar Surya Saputra, Dhanar Surya Didit Suhartono Dilliana Tugas Setiyawan Dwi Angesti Dinda Parameswara Eko Priyanto El Emary, Ibrahiem M. M. Emha Taufiq Luthfi Erina Setyawati Erliyana Nurrahma Fandy Setyo Utomo Fiby Nur Afiana Fikri Ramadhan Giat Karyono Harimanto, Bambang Harimato, Bambang Hasri Akbar Awal Rozaq Hendra Marcos Imam Tahyudin Imam Tahyudin Ipo Novianto istiqomah istiqomah Junindra Yuga Pamungkas Khoirul Abbi Rokhman M. Suyanto Mardiyanto Mardiyanto Melia Dianingrum Min Tsai Lai Mukhlisin Mukhlisin Murtiyoso Murtiyoso Nanjar, Agi Nur Aini Nurdiyansa, Zhafran Afif Oktavia Mulyo Nurdiyanti Prastyo, Priyo Agung Prayoga, Fandhi Dhuga Primandani Arsi Purwadi Purwadi Putri, Eka Ardiya Rahman Rosyidi Rinda Utami Rizki Wahyudi Rona Sepri Ananda Rujianto Eko Saputro Rujiyanto Eko Saputro Saekhu, Ahmad Santi Dwi Astuti Saputra, Alfin Nur Aziz Sarmini - Sarmini Setiyawan, Dillyana Tugas Seto Wahyu Widayat, Seto Wahyu Slamet Endro Prianto Slamet Widodo Taqwa Hariguna Taqwa Hariguna Taqwa Hariguna Tri Astuti Tri Wahyuningsih Trisna Maulida Udianti, Asih Wahid, Arif Mu’amar Wenti Risma Damayanti Wiga Maulana Baihaqi Yuli Purwati Yusuf Nur Alam Yuwono Wisudo Pramono Zanuar Rifai Zhafran Afif Nurdiyansah