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All Journal Information Technology and Telematics Dinamik Jupiter Publikasi Eksternal Jurnal Buana Informatika Pixel : Jurnal Ilmiah Komputer Grafis JUITA : Jurnal Informatika Proceeding SENDI_U Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknik Informatika UNIKA Santo Thomas INTECOMS: Journal of Information Technology and Computer Science JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Aiti: Jurnal Teknologi Informasi Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) International Journal of Social Learning (IJSL) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Informatika Teknologi dan Sains (Jinteks) Maritime Park: Journal Of Maritime Technology and Socienty Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Eduvest - Journal of Universal Studies Seminar Nasional Teknologi dan Multidisiplin Ilmu Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) INOVTEK Polbeng - Seri Informatika
Eri Zuliarso
Universitas Stikubank
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Other

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Predictive Analysis of Student Academic Performance Using Ensemble Learning Methods: A Case Study on the Portuguese Student Performance Dataset Hakim, Mujibul; Zuliarso, Eri; Hidayat, Husni; Imam, Muhammad Nurul; Sholehudin, Mukti Ahmad
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.492

Abstract

The ability to predict student academic performance at an early stage is crucial for educational institutions to provide timely interventions. This research aims to apply and evaluate the effectiveness of ensemble learning methods in predicting the final grades (G3) of secondary school students using the UCI "Student Performance" public dataset. To prevent data leakage, the models were executed without incorporating historical grade variables (G1 and G2), ensuring the system functions strictly as an Early Warning System. The methodological training process was enhanced by integrating k-fold cross-validation,hyperparameter optimization, and a direct comparison against a baseline model (Linear Regression) to guarantee model robustness and validity. Evaluation results indicate that the XGBoost model achieved the highest performance, yielding an Rsquared ($R^2$) of 0.28. Furthermore, feature importance analysis revealed that accumulated absences and prior class failures are the most significant predictors. As a practical implication, these findings recommend that schools develop proactive early warning dashboards and improve the overall school climate to address the root causes of absenteeism at an early stage.
Expert System for Diagnosing Gourami Fish Diseases Using the Certainty Factor Approach Hindayati Mustafidah; Ilham Gunadi; Cahyono Purbomartono; Suwarsito Suwarsito; Eri Zuliarso
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.26031

Abstract

Gourami is an economically significant fish in the aquaculture sector due to its high market demand and relatively stable price. However, it is also challenging to cultivate, with disease outbreaks being one of the primary difficulties. Early diagnosis of gourami fish diseases requires expertise from fish health specialists, who are often difficult to find due to their limited availability. With advancements in artificial intelligence-based technology, this study developed an expert system to diagnose gourami fish diseases based on observed symptoms. The system employs the Certainty Factor (CF) approach to estimate the likelihood of a particular disease affecting the fish. The Certainty Factor approach utilizes a knowledge base derived from expert knowledge to address uncertainty in diagnosis. The certainty factor weights are determined based on confidence levels from both experts and users to generate an accurate diagnosis. This expert system was developed using data from 20 types of gourami fish diseases and 38 associated symptoms. The system successfully identified diseases with a certain level of confidence and provided appropriate treatment recommendations based on the confidence level obtained. By implementing this expert system, the risk of disease outbreaks can be minimized, thereby improving efficiency and productivity in gourami fish farming while helping maintain fish health and reducing economic losses caused by disease.
PENDAMPINGAN PENINGKATAN KOMPETENSI GURU MGMP REKAYASA PERANGKAT LUNAK DALAM PEMANFAATAN TEKNOLOGI KECERDASAN BUATAN UNTUK PEMBELAJARAN Sulastri Sulastri; Eri Zuliarso; Dwi Agus Diartono; Agus Prasetyo Utomo; Wiwien Hadikurniawati
Intimas Vol 6 No 1 (2026)
Publisher : Fakultas Teknologi Informasi dan Industri Unisbank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/intimas.v6i1.10448

Abstract

Di era digital, guru Musyawarah Guru Mata Pelajaran (MGMP) Rekayasa Perangkat Lunak (RPL) menghadapi tantangan dalam mengoptimalkan Kecerdasan Buatan (AI) karena keterbatasan pemahaman dan pelatihan yang aplikatif. Kegiatan pengabdian ini bertujuan untuk meningkatkan literasi teknologi dan kompetensi praktis guru MGMP RPL dalam mengimplementasikan AI (seperti ChatGPT, Canva AI) dan platform digital untuk menciptakan pembelajaran inovatif sesuai Kurikulum Merdeka. Program ini dilaksanakan dengan metode partisipatif dan aplikatif, meliputi lokakarya intensif serta pendampingan daring, di mana peserta membuat produk pembelajaran digital. Hasilnya menunjukkan dampak yang sangat positif: terjadi peningkatan pengetahuan guru tentang AI rata-rata sebesar 37%, tingkat kepuasan peserta mencapai 93%, dan seluruh peserta berhasil menyusun produk pembelajaran interaktif. Lebih dari separuh guru telah menerapkan keterampilan ini di kelas, membuktikan bahwa pelatihan yang sistematis dan praktis efektif dalam meningkatkan kapasitas guru untuk adopsi teknologi..
Strategi Digital Social Branding bagi Panti Jompo dalam Meningkatkan Visibilitas dan Kepercayaan Masyarakat Dwi Budi Santoso; Dewi Handayani Untari Ningsih; Eri Zuliarso; Saefurrohman Saefurrohman; M. Riza Radyanto
Abdimas Galuh Vol 8, No 1 (2026): Maret 2026
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/ag.v8i1.21967

Abstract

Pengabdian kepada masyarakat ini bertujuan untuk meningkatkan visibilitas dan kepercayaan publik terhadap panti jompo melalui penerapan strategi digital social branding. Kegiatan dilakukan di Pusat Jagaan dan Rawatan Orang Tua Al-Ikhlas, Puchong, Selangor, Malaysia, yang menghadapi permasalahan rendahnya citra digital dan belum optimalnya penggunaan media sosial sebagai sarana komunikasi publik. Metode pelaksanaan meliputi observasi lapangan, wawancara dengan pengelola, pelatihan tatap muka, mentoring, serta pendampingan penyusunan konten digital dan pengembangan identitas merek digital. Materi pelatihan mencakup konsep dasar branding, penguatan identitas digital, komunikasi etis, teknik digital storytelling, serta penggunaan model konten 70:20:10 untuk menjaga konsistensi pesan. Hasil kegiatan menunjukkan peningkatan pemahaman mitra dalam membangun citra positif melalui narasi digital, penggunaan media sosial, dan pembuatan konten visual yang humanis dan beretika. Mitra mulai mampu membuat storyboard, poster digital, serta konten foto dan video pendek yang layak dipublikasikan. Selain itu, mitra berhasil menyusun brand message yang menonjolkan nilai kasih sayang, kepedulian, dan profesionalisme layanan lansia. Pelatihan ini memberikan dampak positif berupa meningkatnya kemampuan pengelola dalam mengelola citra digital secara mandiri. Kegiatan ini menyimpulkan bahwa digital social branding merupakan strategi efektif untuk membangun kepercayaan publik, meningkatkan reputasi institusi, dan memperluas jangkauan komunikasi panti jompo. Direkomendasikan adanya pendampingan lanjutan agar praktik branding digital dapat diterapkan secara konsisten dan berkelanjutan.
Transformasi Keterampilan Desain Digital Berbasis UI/UX: Pelatihan dan Pendampingan untuk Siswa SMK dalam Meningkatkan Daya Saing Industri Wismarini T.D.; Aji Supriyanto; Eri Zuliarso; Budi Hartono
JURPIKAT Vol 6 No 4 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v6i4.2094

Abstract

Program pengabdian ini berfokus pada peningkatan keterampilan desain digital berbasis UI/UX bagi siswa SMK Negeri 4 Semarang sebagai respons terhadap terbatasnya pemahaman dan kemampuan siswa dalam bidang ini, yang menjadi hambatan utama dalam persaingan kerja di era digital. Tujuan utama kegiatan ini adalah membekali siswa dengan pengetahuan dan keterampilan praktis UI/UX melalui pelatihan intensif dan pendampingan proyek berbasis tim. Metode yang digunakan meliputi pelatihan terstruktur, bimbingan praktis dalam pembuatan prototipe interaktif, serta evaluasi berkelanjutan untuk mengukur pemahaman siswa. Hasil pengabdian menunjukkan peningkatan signifikan pada kemampuan siswa dalam menerapkan prinsip UI/UX, baik dalam proyek individu maupun kolaboratif. Dampaknya, siswa kini memiliki portofolio digital yang memperkuat daya saing mereka di industri kreatif dan teknologi, serta membuka peluang magang dan pekerjaan di sektor terkait.
Analysis of Service Quality Improvement Strategies for the PTSP Online System using E-SERVQUAL and Importance–Performance Analysis (IPA) Eko Ariyanto; Eri Zuliarso
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6610

Abstract

The digital transformation of higher education has increased the demand for high-quality, efficient, and user-centered electronic services. Sunan Kudus State Islamic University has implemented the Online One-Stop Integrated Service (PTSP Online) system to support academic and administrative service delivery. However, a comprehensive evaluation of its service quality from the users' perspective has not yet been conducted. This study aims to evaluate the service quality of PTSP Online, identify the gaps between users' expectations and perceptions, and determine service improvement priorities as a basis for developing strategies to enhance digital service quality. A quantitative research approach was employed using the E-SERVQUAL and Importance–Performance Analysis (IPA) methods. Data were collected through a questionnaire survey involving 382 users who had previously accessed PTSP Online for academic services. The E-SERVQUAL analysis was conducted to measure the service quality gaps between users' perceptions and expectations across five dimensions: Efficiency, System Availability and Reliability, Responsiveness, Privacy Security Assurance, and Contact and Ease of Use. Subsequently, Importance–Performance Analysis (IPA) was applied to map service attributes according to their perceived importance and performance. The results indicate that the overall service quality of PTSP Online is satisfactory, with relatively small gap values across all dimensions. The Responsiveness dimension exhibited the highest positive gap (0.06), whereas Efficiency recorded the largest negative gap (−0.03). The IPA results identified service data and information accuracy (SAR4) as the highest priority for improvement, while nine service attributes were positioned in Quadrant II (Keep Up the Good Work). This study extends the application of the integrated E-SERVQUAL and IPA approach to the context of digital administrative services in Islamic higher education institutions, an area that remains underexplored in the existing literature. Furthermore, the findings provide empirical evidence to support PTSP Online administrators in formulating sustainable strategies for improving digital service quality.
Application of Machine Learning in Analyzing Bandwidth Usage Patterns for Internet Service Providers Alfin Hilmy Nurmakhlufi; Eri Zuliarso
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/h2p5s858

Abstract

This study aims to address bandwidth management challenges faced by Internet Service Providers (ISP) through the application of machine learning techniques for analyzing usage patterns and forecasting future demand. A key novelty of this research lies in the combined use of K-Means clustering for dynamic customer segmentation based on real-time utilization patterns, followed by accurate short-term forecasting using Random Forest regression, specifically tailored for corporate client bandwidth planning. Data was collected from 12 corporate customers over a three-month period (January–March 2025) at five-minute intervals using the PRTG Network Monitor. The analytical workflow included data preprocessing, customer segmentation using K-Means clustering, and short-term bandwidth prediction using Random Forest regression. The clustering results classified customers into three main categories: underutilized, optimal, and overutilized, with a silhouette score of 0.663 indicating good cluster separation. The regression model achieved a coefficient of determination (R²) of 0.931, a Mean Absolute Error (MAE) of 0.036 Mbps, and a Root Mean Square Error (RMSE) of 0.062 Mbps, demonstrating high predictive accuracy for operational planning. This study is limited by the relatively short observation period and the exclusion of external variables in the modeling process. For future work, the use of deep learning methods such as Long Short-Term Memory (LSTM) or Temporal Convolutional Networks (TCN) is recommended, along with the integration of external features such as time-based traffic anomalies and customer profiles, to enhance model robustness, accuracy, and generalization.
Rainfall Prediction using the SARIMAX and LSTM Methods in Semarang City Rudi setyo P; Eri Zuliarso
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/6sg7m889

Abstract

The purpose of this study is to predict the decade rainfall in Semarang City using two main methods, namely Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) and Long Short Term Memory (LSTM). The methodology of this study begins with data preprocessing, which includes data deletion analysis using dropna and data normalization using Min-Max Scaling to reduce the scale to between 0 and 1. The dataset is then divided into 80% training data and 20% test data. The validity of the data (X_test, Y_test) using the best 56-epoch data validation (val_loss) is better than the validity of the training data (loss). On the other hand, SARIMAX uses the (2,1,2), (2,1,2,36) model, and its validation techniques include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). Specifically, the RMSE of the LSTM model is 19.6, and the RMSE of the SARIMAX is 31.05. The MAE of LSTM is 15.0, SARIMAX is 24.5, the R2 of LSTM is 0.814, and SARIMAX is 0.52. Lower RMSE and MAE values indicate lower prediction errors, but a higher R2 value of 1 indicates that LSTM can explain 81% of the actual data variation, which is better than SARIMAX, which is only about 52%. The main finding of this study is that the LSTM model performs better when recommending rainfall datasets.
Decade Rainfall Prediction Using Prophet Algorithm and LSTM (Case Study in Banjarnegara Regency) Sulistiyowati sulis; Eri Zuliarso
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/j3mbxq89

Abstract

Hydrometeorological disasters such as floods and landslides in Banjarnegara Regency are closely related to fluctuating rainfall variability. This study aims to predict decadal (10-day) rainfall by comparing the performance of the Prophet algorithm and the Long Short-Term Memory (LSTM) model. The dataset comprises daily rainfall records from 14 observation stations spanning the period 2005–2024. The research stages included preprocessing, modelling, hyperparameter optimization using Optuna, and evaluation with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results indicate that the Prophet model outperformed LSTM in most locations, with an average RMSE of 69.55 and MAE of 53.05, lower than LSTM, which recorded 73.03 and 55.72, respectively. The ensemble averaging model produced competitive results at several locations, although it was less responsive to sharp fluctuations in rainfall. These findings confirm that Prophet is more effective in capturing seasonal patterns and long-term trends, thus providing significant potential to support climate-based disaster mitigation systems in vulnerable areas such as Banjarnegara
SEGMENTASI PROBABILISTIK CALON MAHASISWA BERBASIS INDOBERT, GAUSSIAN MIXTURE MODEL, DAN LLM DI ITSNU PEKALONGAN Mujibul Hakim; Eri Zuliarso
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8370

Abstract

Private higher education institutions face challenges in understanding shifting prospective-student characteristics driven by socio-economic dynamics, including the COVID-19 pandemic. ITSNU Pekalongan recorded a non-linear enrollment pattern in 2019–2024 (from 146 to a peak of 680; N = 2,362). This study aims to analyze probabilistic segmentation, measure cross-period structural breaks, build an LLM-based analysis automation system, and validate it through expert judgement. A Research and Development approach follows a ten-stage CRISP-DM framework: IndoBERT (768 dimensions), PCA, Gaussian Mixture Model (GMM) with diag covariance and k-means++ init, Adjusted Rand Index (ARI), and a Hybrid Cognitive Pipeline (local Llama 3.2 and OpenRouter cloud). The results show that 4 of 5 inter-period transitions are structural breaks (ARI < 0.30), identical-entity cosine similarity of IndoBERT embeddings averages 0.9234 (range 0.8932–0.9426), and expert validation reaches 4.0/5.0 for a 2025 projection of 592 applicants. The IndoBERT–GMM–LLM integration yields a replicable recruitment decision-support system.
Co-Authors . Sulastri . Suwarsito Aditya Bobby Rizki Agus Prasetyo Utomo Ahmad Fathoni Aji Supriyanto Alfin Hilmy Nurmakhlufi Allaam, Ekananda Naufal Amalina, Hana Anefia Mutiara Atha ariadi, hastomo Arief Jananto Ariyani, Dewi Ayu Arya Sena Setyanegara Astuti, Vivi Rizki Indri Bambang Wiranto, Joko Budi Hartono Dewi Handayani Untari Ningsih Diah Lisdianti Dian Kristiawan Nugroho Dwi Agus Diartono Dwi Budi Santoso Dwi Budi Santoso Edy Winarno Eko Ariyanto Fathoni, Aliffatul Majid Februarianti, Herny Fidiniari, Fathia Hakim, Mujibul Hastomo Ariadi Heri Maryanto, Cahyono Purbomartono, Heri Maryanto, Hermawan, Taufan Herny Februariyanti Herny Februariyanti Hersatoto Listiyono Hindayati Mustafidah Husni Hidayat Ilham Gunadi Indah Widhi Prastika Indriani, Cheryllista Isworo Nugroho Khabib Mustofa Kogoya, Erminus Kusuma Satria, Hafiyan Nafan M. Riza Radyanto Mardi Siswo Utomo Muhammad Nurul Imam, Muhammad Nurul Mujibul Hakim Munna, Aliyatul Nur Rohim Nurmakhlufi, Alfin Hilmy Priambodo, Wisnu Putra Alva, Ilyasa Garuda Putri, Indah Lissiana R. Soelistijadi Radyanto, Mohammad Riza Rara Sri Artati Redjeki Ratmoko, Hari Rezal Arminto, Edo Rina Candra NS Rizky Abdul Malik Rosyida, Elviana Rudi setyo P Rudi setyo P Ruslana, Zauyik Nana S Sunardi Saefurrohman Saefurrohman Safra, Icha Adellia Safra, Kyla Kaneshia Sarah Husain Toman Sariyun Naja Anwar Sholehudin, Mukti Ahmad Sri Eniyati Sri Hartati Sudiantoro, Adhi Viky Sugiyamto Sugiyamto, Sugiyamto Sulastri Sulastri Sulastri Sulastri Sulatri sulis, Sulistiyowati Sulistiyowati sulis Sunardi Sunardi Velamentosa, Desvio Wahyu Prasetyo Wibowo, Sayogo Wismarini T.D. Wismarini, Th Dwiati Wiwien Hadikurniawati Yassin Achmad Nur Aziz Yohanes Suhari Yunus Anis Yunus Anis, Yunus Yuwan, Ridho Pangestu Zulfa Febriana Dewi Mellinia