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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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A Systematic Review of Retrieval-Augmented Generation for Enhancing Domain-Specific Knowledge in Large Language Models Murtiyoso, Murtiyoso; Tahyudin, Imam; Berlilana, Berlilana
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14824

Abstract

This literature review examines the use of Retrieval-Augmented Generation (RAG) in enhancing Large Language Models (LLM) for domain-specific knowledge. RAG integrates retrieval techniques with generative models to access external knowledge sources, addressing the limitations of LLMs in handling specialized information. By leveraging external data, RAG improves the accuracy and relevance of generated content, making it particularly useful in fields that require detailed and up-to-date knowledge. This review highlights the effectiveness of RAG in overcoming challenges such as data sparsity and the dynamic nature of specialized knowledge. Furthermore, it discusses the potential of RAG to enhance LLM performance, scalability, and the ability to generate contextually accurate responses in knowledge-intensive applications. Key challenges and future research directions in the implementation of RAG for domain-specific knowledge are also identified.
Comparative Analysis of DBSCAN, OPTICS, and Agglomerative Clustering Methods for Identifying Disease Distribution Patterns in Banjarnegara Community Health Centers Setiyawan, Dillyana Tugas; Berlilana, Berlilana; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4577

Abstract

The variation in disease distribution patterns across community health centers in Banjarnegara Regency necessitates a precise segmentation analysis to support effective allocation of healthcare resources. This study aims to compare the effectiveness of three clustering methods DBSCAN, OPTICS, and Agglomerative Clustering in grouping Puskesmas based on the type and number of diseases they manage. The evaluation methods used include the Silhouette Score and the Davies-Bouldin Index, which assess the quality of the clustering results. The analysis indicates that Agglomerative Clustering produces the most stable cluster structures, reflected in its highest Silhouette Score, compared to DBSCAN and OPTICS, which tend to yield more noise and less optimal clustering quality. These findings suggest that hierarchical clustering approaches are more effective in the context of healthcare service distribution data at the primary care level. The results of this study are expected to serve as a foundation for the formulation of data-driven and region-based health policies, particularly in designing more targeted interventions and optimizing the distribution of healthcare services.
Analisis Faktor-Faktor Penerimaan Teknologi dalam Pembelajaran Vokasi: Integrasi Model Technology Acceptance Model dan Theory of Planned Behavior di SMK Ma’arif 1 Kroya Harimato, Bambang; Berlilana, Berlilana; Barkah, Azhari Shouni
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 8 (2025): JPTI - Agustus 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.772

Abstract

Penerapan teknologi dalam pendidikan vokasi menjadi semakin penting untuk menunjang kesiapan siswa menghadapi era digital dan industri 4.0. Namun, tingkat penerimaan teknologi oleh siswa, khususnya di jurusan Teknik Komputer dan Jaringan (TKJ), masih menunjukkan variasi yang signifikan dan membutuhkan kajian lebih lanjut. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi penerimaan teknologi dalam pembelajaran vokasi di SMK Ma’arif 1 Kroya, dengan mengintegrasikan pendekatan Technology Acceptance Model (TAM) dan Theory of Planned Behavior (TPB). Penelitian ini menggunakan metode kuantitatif dengan teknik analisis Partial Least Squares Structural Equation Modeling (PLS-SEM) melalui perangkat lunak SmartPLS 4. Hasil analisis menunjukkan bahwa PU dan PEU berpengaruh signifikan terhadap sikap siswa terhadap penggunaan teknologi (ATT), sedangkan SN dan PBC berkontribusi langsung terhadap niat penggunaan (BI). Selanjutnya, BI terbukti berpengaruh terhadap penggunaan aktual teknologi dalam pembelajaran (AU). Temuan ini mengindikasikan bahwa strategi peningkatan penerimaan teknologi perlu difokuskan pada penguatan persepsi kegunaan dan kemudahan teknologi, dukungan sosial, serta pemberdayaan kontrol perilaku siswa. Implikasi praktis dari penelitian ini mencakup pengembangan pelatihan teknologi bagi siswa dan guru, serta desain pembelajaran vokasi yang berbasis teknologi secara lebih interaktif dan aplikatif.
Analysis of Technology Adoption Factors in Learning among Vocational Students using UTAUT2 Model Harimanto, Bambang; Berlilana, Berlilana; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4940

Abstract

Technology acceptance in vocational education is a key factor in supporting the effectiveness of teaching and learning processes in the digital era. This study aims to analyze the factors influencing technology acceptance among students of the Computer and Network Engineering (TKJ) Department at SMK Ma'arif 1 Kroya using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. The model includes the variables Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, Habit, Behavioral Intention, and Actual Usage. The results reveal that five key variables—Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, and Price Value—significantly influence Behavioral Intention, while Habit, Facilitating Conditions, and Behavioral Intention directly affect Actual Usage. All constructs in the model meet validity and reliability criteria, and no multicollinearity was detected (VIF < 3.3). The coefficient of determination (R²) values of 0.612 for Behavioral Intention and 0.673 for Actual Usage indicate strong predictive power of the model. These findings confirm the relevance of the UTAUT2 framework for understanding and enhancing technology acceptance in vocational education settings and provide valuable insights for improving technology integration in technical learning environments.
Implementasi Simple Additive Weighting dan Weighted Product pada Sistem Pendukung Keputusan untuk Rekomendasi Penerima Beras Sejahtera Berlilana, Berlilana; Prayoga, Fandhi Dhuga; Utomo, Fandy Setyo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 4: Agustus 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2428.333 KB) | DOI: 10.25126/jtiik.201854768

Abstract

Salah satu upaya pemerintah untuk mengatasi masalah kemiskinan di Indonesia yaitu membuat program beras sejahtera (RASTRA). RASTRA merupakan program dari pemerintah berupa bantuan beras bersubsidi untuk membantu masyarakat yang berpenghasilan rendah. Permasalahan yang terjadi yakni banyaknya kriteria penilaian yang digunakan dalam pedoman RASTRA dan penduduk miskin di suatu area/wilayah seringkali menyulitkan proses penentuan Keluarga Penerima Manfaat yang berhak menerima RASTRA pada Musyawarah desa/kecamatan. Tujuan penelitian ini adalah merancang dan mengembangkan sistem penunjang keputusan menggunakan model matematika Simple Additive Weighting (SAW) dan Weighted Product (WP) untuk memberikan rekomendasi penerima RASTRA. Terdapat empat tahapan penelitian yang digunakan untuk mencapai tujuan penelitian, yaitu analisis kebutuhan perangkat lunak, desain perangkat lunak, pengembangan, dan pengujian perangkat lunak. Berdasarkan hasil pengujian, hasil perhitungan nilai preferensi SAW memiliki performa yang lebih baik daripada WP karena SAW mampu meminimalisir nilai preferensi alternatif yang sama. Hal ini tampak dari perankingan alternatif berdasarkan hasil perhitungan SAW sejumlah 13 peringkat, dan WP sejumlah 10 peringkat. AbstractOne of the government's efforts to overcome the poverty problem in Indonesia is to make the program "Beras Sejahtera" (RASTRA). RASTRA is a government program of subsidised rice to help low-income communities. The problems which occur are the number of assessment criteria used in the RASTRA guidelines and the poor in an area/region often complicate the process of determining the Beneficiary Family who are eligible to receive RASTRA at the village/sub-district deliberation. The purpose of this research is to design and develop decision support system using Simple Additive Weighting (SAW) and Weighted Product (WP) mathematical model to give the recommendation of RASTRA recipient. There are four research stages to achieve the research objectives, namely software requirements analysis, software design, development, and software testing. Based on the test results, the calculation of SAW preference values has better performance than WP because SAW can minimise the value of the same alternative preferences. This can be seen from the alternative ranking based on the calculation of SAW of 13 ranks, and WP 10 rank number.
Comparative Analysis of Data Balancing Techniques for Machine Learning Classification on Imbalanced Student Perception Datasets Saekhu, Ahmad; Berlilana, Berlilana; Saputra, Dhanar Intan Surya
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4286

Abstract

Class imbalance is a common challenge in machine learning classification tasks, often leading to biased predictions toward the majority class. This study evaluates the effectiveness of various machine learning algorithms combined with advanced data balancing techniques in addressing class imbalance in a dataset collected from Class XI students of SMK Ma'arif 1 Kebumen. The dataset, comprising 300 instances and 36 features, includes textual attributes, demographic information, and sentiment labels categorized as Positive, Neutral, and Negative. Preprocessing steps included text cleaning, target encoding, handling missing data, and vectorization. Four sampling techniques—SMOTE, SMOTE + Tomek Links, ADASYN, and SMOTE + ENN—were applied to the training data to create balanced datasets. Nine machine learning algorithms, including CatBoost, Extra Trees, Random Forest, Gradient Boosting, and others, were evaluated using four train-test splits (60:40, 70:30, 80:20, and 90:10). Model performance was assessed using metrics such as accuracy, precision, recall, F1-score, and AUC- ROC. The results demonstrate that SMOTE + Tomek Links is the most effective balancing technique, achieving the highest accuracy when paired with ensemble algorithms like Extra Trees and Random Forest. CatBoost also delivered competitive performance, showcasing its adaptability in imbalanced scenarios. The 90:10 train-test split consistently yielded the best results, emphasizing the importance of adequate training data for model generalization. This study highlights the critical role of data balancing techniques and robust algorithms in optimizing classification performance for imbalanced datasets and provides a framework for future research in similar contexts.
Enhancing Clustering Performance through Benchmarking of Dimensionality Reduction Techniques on Educational Data Priyanto, Eko; Berlilana, Berlilana; Tahyudin, Imam
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4297

Abstract

This study evaluates the effectiveness of dimensionality reduction techniques in enhancing clustering performance using a tracer study dataset of 500 alumni from UMNU Kebumen, containing 58 variables. The objective was to identify the optimal combination of dimensionality reduction and clustering methods for uncovering patterns in alumni profiles, job search strategies, and employment outcomes. Principal Component Analysis (PCA), Non- Negative Matrix Factorization (NMF), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) were applied, followed by clustering using K-Means, DBSCAN, and Hierarchical Clustering. The findings revealed that NMF achieved the highest clustering quality, particularly with K- Means and Hierarchical Clustering, outperforming PCA. NMF also demonstrated superior compactness with a Calinski-Harabasz Index of 287.96, compared to 125.88 for PCA. While t-SNE and UMAP delivered competitive results, their computational times of 245.8 and 76.5 seconds, respectively, made them less practical for large datasets. The novelty of this study lies in its comprehensive evaluation of dimensionality reduction techniques and the integration of diverse clustering algorithms to assess their interplay. The results provide actionable insights, recommending NMF for accuracy-critical tasks and PCA for time-sensitive applications. Given the increasing volume of high-dimensional educational data, this study highlights the critical need for efficient clustering strategies to extract meaningful insights, ultimately supporting data-driven decision-making in education and workforce planning. Addressing these challenges is essential to optimizing institutional strategies, improving student employability, and enhancing workforce alignment with industry demands.
Implementation of Analytical Hierarchy Process (AHP) Method on Teacher Performance Appraisal Decision Support System Erliyana Nurrahma; Berlilana, Berlilana
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 3 (2024): October 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.3.2024.330-343

Abstract

Assessing the quality of education in a school often hinges on evaluating the performance of its teaching staff. Teachers play a pivotal role as professional educators shaping the learning experiences of students. Their performance evaluations not only serve as a benchmark for excellence but also as criteria for career advancements, such as promotions to higher positions or recommendations for teacher certification programs. To ensure these assessments are fair, consistent, and objective, it's crucial to employ a reliable evaluation method. The Analytical Hierarchy Process (AHP) emerges as a robust tool for this purpose. AHP offers a structured framework that facilitates comprehensive decision-making by allowing decision-makers to prioritize various criteria based on their relative importance. In the context of this study, data for the AHP analysis was gathered through questionnaires distributed to respondents. These questionnaires likely covered multiple aspects of teaching performance, such as instructional effectiveness, classroom management, and professional development. After collecting and analyzing the data using the AHP method, the results provided a weighted ranking of the teachers. According to the AHP analysis, Teacher C emerged as the top performer with a weight of 0.7604 or 76.04%. This indicates that Teacher C excelled in the evaluated criteria and demonstrated superior teaching skills. Following closely, Teacher B secured the second spot with a weight of 0.2079 or 20.79%. Lastly, Teacher A received the lowest priority with a weight of 0.0517 or 5.17%. These findings not only highlight the strengths and areas for improvement among the teachers but also offer valuable insights for school administrators to make informed decisions regarding promotions, certifications, and professional development opportunities.
Comparative Performance Analysis of Random Forest and Logistic Regression for Sentiment Classification of the Makan Bergizi Gratis Program on Platform X Slamet Endro Prianto; Berlilana Berlilana; Rujianto Eko Saputro
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.v8i1.1371

Abstract

The rapid growth of e-commerce has made personalized product recommendations a crucial aspect of enhancing customer satisfaction and boosting sales. However, many small-to-medium-sized retail businesses, like Adiva Fashion Store, still rely on manual product selection through customer searches or seller recommendations, which often leads to challenges in meeting customer preferences. This study presents a case study of Adiva Fashion Store, where the Collaborative Filtering method was implemented to develop a personalized clothing product recommendation system. The item-based Collaborative Filtering approach was employed to calculate the similarity between products based on customer ratings and transaction history. These similarity values were then used to predict customer preferences for products that had not yet been purchased. The system was developed using the Waterfall methodology, which involved needs analysis, system design, implementation, testing, and maintenance. The results show that the recommendation system significantly improved the relevance of product suggestions, helping customers make better purchasing decisions and increasing sales effectiveness. This case study illustrates how data-driven recommendation systems can be effectively integrated into small-to-medium-sized retail environments, providing valuable insights for other businesses aiming to adopt similar strategies.
A Decision Support System for Assessing High School Students' Soft Skills Using the Analytical Hierarchy Process Yuwono Wisudo Pramono; Berlilana Berlilana; Azhari Shouni Barkah
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.v8i1.1420

Abstract

Assessing students' soft skills in educational settings is often challenging due to the subjectivity and inconsistency inherent in evaluating qualitative traits. This study employs the Analytical Hierarchy Process (AHP) as a decision support tool to provide a more systematic, consistent, and objective method for evaluating students' soft skills. The assessment model is based on four key criteria—critical thinking, communication, collaboration, and creativity—each further broken down into measurable subcriteria. The study was conducted at MA Mu’allimin Sruweng Kebumen, where evaluations were carried out by a guidance and counseling teacher acting as an expert evaluator, using a numerical scale ranging from 1 to 100. Pairwise comparison matrices were developed using Saaty’s fundamental scale to determine the weights for both criteria and subcriteria, followed by consistency testing using the Consistency Ratio (CR). The findings reveal that critical thinking and collaboration were assigned the highest priority weights, with all comparison matrices meeting the acceptable consistency threshold. The resulting global preference values offer a more objective, proportional representation of students’ soft skills achievements. This AHP-based model enables fairer, more consistent evaluations and provides quantitative outputs that can be utilized for student ranking and structured feedback in educational decision-making.
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