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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Transmisi: Jurnal Ilmiah Teknik Elektro JURNAL SISTEM KOMPUTER JURNAL SISTEM INFORMASI BISNIS TRANSIENT: Jurnal Ilmiah Teknik Elektro Jurnal Sistem Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor Jurnal Teknologi Rekayasa Mesin Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Scientific Journal of Informatics Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika JURIKOM (Jurnal Riset Komputer) Journal of Information Systems and Informatics Jurnal Ilmu Teknik dan Komputer Jurnal Abdi Insani Infotek : Jurnal Informatika dan Teknologi Journal of Applied Data Sciences SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) Journal of Computing Theories and Applications Jurnal Profesi Insinyur Indonesia Jurnal Teknologi Siber International Journal of Advanced Law
R Rizal Isnanto
Departemen Teknik Sistem Komputer, Universitas Diponegoro Jl. Prof. Sudharto, SH, Kampus UNDIP Tembalang, Semarang 50275, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Transportation

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Sistem Pakar Diagnosis Penyakit Gastritis Menggunakan Metode Certainty Factor Berbasis Android Windarto, Yudi Eko; Isnanto, R Rizal; Setiawan, Annas
Jurnal Transformatika Vol. 18 No. 1 (2020): July 2020
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v18i1.2088

Abstract

With the increasingly of the modern era, more and more diseases that arise due to human lifestyle and bacterial transmission. One of them is gastritis which occurs because of inflammation that occurs in the lining of the stomach which makes frequent pain in the stomach. From these problems, it is necessary to make an expert system application of gastritis disease which aims to provide information to the public to know the early symptoms of gastritis from an early age. The process of diagnose using the certainty factor method The methods in this study are data retrieval, system design, system making and system testing.From this research, it produces an expert system application that can diagnose the type of disease with the first test case 98.848%, the second test case 99.8464% and the third test case with the results of 99.99115264% correct and in accordance with expert knowledge. System testing is done by using black-box testing that shows the function of each unit of application 100% successful.
Klasifikasi Jenis Ikan Laut K-Nearest Neighbor Berdasarkan Ekstraksi Ciri 2-Dimensional Linear Discriminant Analysis Al Iman, Yusraka Dimas; Isnanto, R Rizal; Nurhayati, Oky Dwi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106787

Abstract

Indonesia adalah suatu negara kepulaun yang memiliki 2/3 wilayah lautan, secara sektor indonesia memiliki potensi pangan yang sangan besar dalam sektor perikanan. Ikan di dunia yang berhasil diuraikan sebanyak 27.000 terutama paling banyak dilaut indonesai. Ikan adalah salah satu keanekaragaman biologi yang menyusun ekosistem bahari. Ikan mempunyai bentuk serta ukuran eksklusif yang berbeda jenis yang satu dangan jenis yang lain. Pengenalan spesies ikan umumnya dilakukan secara manual dengan pengamatan mata. Tujuan penelitian ini untuk mengenali spesies ikan laut. 2-Dimensional Linear Discriminant Analysis (2D-LDA) dipergunakan untuk ekstraksi ciri dan K-Nearest Neighbor (K-NN) dipergunakan untuk klasifikasi jenis ikan laut. Fitur 2-Dimensional Linear Discriminant Analysis (2D-LDA) yang diekstraksi untuk menghasilkan dua matrik baru yaitu matrik score. Klasifikasi menggunakan metode K-Nearest Neighbor (K-NN) dengan membandingkan nilai k-n. Penelitian ini menggunakan 5 jenis ikan laut, dengan total data latih 800 gambar dan data uji 160 gambar. Hasil percobaan tebaik diperoleh k-9 dengan tingkat akurasi terbaik sebesar 93,12%, presisi 82,05%, recall 100%, dan F-1 score 90,14%.AbstractIndonesia is an archipelagic country which has 2/3 of the sea area, in terms of sector Indonesia has enormous food potential in the fisheries sector. There are 27,000 fish in the world that have been successfully described, especially in the Indonesian seas. Fish is one of the biological diversity that makes up the marine ecosystem. Fish have specific shapes and sizes that differ from one type to another. The identification of fish species is generally done manually by eye observation. The purpose of this research is to identify marine fish species. 2-Dimensional Linear Discriminant Analysis (2D-LDA) is used for feature extraction and K-Nearest Neighbor (K-NN) is used for classification of marine fish species. The 2-Dimensional Linear Discriminant Analysis (2D-LDA) features were extracted to produce two new matrices, namely the score matrix. The classification uses the K-Nearest Neighbor (K-NN) method by comparing the k-n values. This study used 5 types of marine fish, with a total of 800 images of training data and 160 images of test data. The best experimental results were obtained by k-9 with the best accuracy rate of 93.12%, precision of 82.05%, recall of 100%, and F-1 score of 90.14%.
ANALYSIS OF FIXED CARBON AND VOLATILE MATTER BRIQUETTES OF PINE SAWDUST AND COCONUT SHELL WASTE Dewi, Rany Puspita; Sumardi, Sumardi; Isnanto, Rizal
Jurnal Rekayasa Mesin Vol. 14 No. 3 (2023)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v14i3.1421

Abstract

Briquetting technology became one appropriate method that can be used to convert biomass waste into a renewable energy source. Sources of biomass raw materials that have promising potential are pine sawdust and coconut shell waste. Sawdust has potential for about 0.78 million m3/year and coconut shell waste around 360 thousand tons/year. The research aim was to analyse the effect of the carbonization temperature to volatile matter and fixed carbon of briquette. The research was done by variating carbonization temperature at 400 oC, 500 oC, and 600 oC. The result showed that at carbonization temperature of 400 oC, the volatile matter and fixed carbon was 42.28% and 55.74%. The volatile matter and fixed carbon are 43.19% and 54.96%, found at carbonization temperature 500 oC. The highest fixed carbon 55.98% and the lowest volatile matter 42.19% was found from carbonization temperature at 600 oC. The carbonization temperature in briquetting process affects the volatile matter and fixed carbon of briquette.
Patterned Dataset Model Optimization to Predict Bitcoin IDR Price using Long Short Term Memory Parlika, Rizky; Isnanto, R Rizal; Rahmat, Basuki
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.4036

Abstract

The goal of this study was to determine the optimal combination for optimizing the Patterned Dataset Model, particularly in patterned datasets during periods of price decline (crash).  In previous research, the Crash Patterned Dataset has been shown to predict the next Bitcoin price. In this study, an experiment was conducted using a combination of prediction models, including ARIMA, machine learning, and deep learning. This research was conducted in 3 stages. The first stage is to compare the error results from the Bitcoin pair IDR crypto asset prediction process, which are part of the stored data from the patterned dataset under crash conditions. This dataset was tested with several prediction models, and the LSTM model with 60 seconds of resampling produced the best results, with an MAPE of 0.19%. In the second stage, BTCIDR, as part of the data from the patterned dataset in crash conditions, was resampled with variants 1D, 2D, 3D, 4D, 5D, 6D, 7D, 1H, 2H, 3H, 4H, 5H, 6H, 7H, 8H, 9H, 10H, 11H, and 12H. The result is that BTCIDR with a 3H resample has the lowest MAPE, at 1.3%. In the third stage, the prediction process is carried out using the LSTM model on the BTC IDR test dataset (as part of the Patterned Dataset in crash conditions) with a 3H resample. The dataset range is from May 2022 to 2025-01-23 11:05:48. This test predicts the Bitcoin IDR price series for the next 30 days, calculates the MAPE between the predicted series and the actual BTC IDR dataset 30 days later, and evaluates the results. The MAPE value for the Bitcoin IDR price prediction was 9.27%. This indicates that the average prediction error against the actual price is around 9.27%. The main objective of this research is to more accurately predict the price of the Bitcoin-IDR pair, providing additional helpful information for trading cryptocurrencies.
PERSEPSI MASYARAKAT TERHADAP PEMASANGAN LAMPU TENAGA SURYA DI DESA KEBONDOWO (PROGRAM IDBU): SURVEI KEPUASAN DAN HAMBATAN Isnanto, R Rizal; Prastawa, Heru; Purbowati, Endang; Setyowati, Ro’fah; Dwidiyanti, Meidiana; Wisnaeni, Fifiana; Setiadji, Bagus Hario; Wijayanto, Dian; Kurnia, Dita Juni
Jurnal Abdi Insani Vol 12 No 12 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i12.2950

Abstract

Global warming and the increasing demand for electricity have strengthened the urgency to introduce environmentally friendly technologies at the community level. Kebondowo Village, located in Banyubiru Sub-district, remains limited in nighttime lighting access and relies heavily on conventional electricity, indicating the need for an efficient and sustainable renewable-energy alternative. In response to this condition, Universitas Diponegoro initiated the Iptek Desa Binaan Undip (IDBU) program through the installation of solar-powered street lights across seven hamlets from July to August 2025 as an early effort to support the development of an environmentally friendly village. The objectives of this program were to improve the quality of village lighting, reduce dependence on conventional energy, increase community awareness of renewable energy use, and evaluate the level of public acceptance of the installed technology. The evaluation was conducted using surveys and semi-structured interviews with 40 adult residents, representing diverse professions and age ranges from 28 to 69 years old (mean age 48), to assess user perceptions, perceived benefits, and expectations for program sustainability. The results showed a highly positive response, with 97% of respondents agreeing with the installation of solar lighting and reporting direct benefits, including improved comfort, safety, and energy efficiency during nighttime activities. Furthermore, most residents expressed their expectation for the expansion of solar-powered street lights across all hamlets. These findings confirm that renewable-energy technology has a high level of social acceptance, effectively improves the community’s quality of life, and holds strong potential to be developed as a sustainable program based on rural community empowerment.  
Beyond Dashboards: A Systematic Literature Review of Learning Analytics, Business Intelligence, and Generative AI for Decision-Making in Universities Heri Purwanto; R. Rizal Isnanto; Qidir Maulana Binu Soesanto; Agus Nursikuwagus; Fahmi Reza Ferdiansyah
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15963

Abstract

The rapid proliferation of learning analytics, business intelligence (BI), artificial intelligence (AI), and generative AI (GenAI) has significantly expanded universities’ ability to collect, integrate, analyze, and operationalize institutional data. However, despite advances in predictive analytics, dashboards, and AI-driven systems, the translation of analytical outputs into consistent and accountable institutional decision-making remains uneven. This systematic literature review synthesizes contemporary research on analytics-enabled decision-making in higher education with the aim of moving beyond dashboard-centric perspectives toward a socio-technical and computing-oriented understanding of how data are transformed into institutional actions and outcomes. Guided by the PRISMA framework, the review synthesizes evidence across four interconnected dimensions: data ecosystems and learning analytics foundations; analytics capability, BI adoption, and digital readiness; AI and advanced analytics for decision support; and human-in-the-loop (HITL) decision routines and institutional outcomes. The findings show that predictive performance and analytical sophistication alone do not guarantee decision value. Instead, effective analytics-enabled decision-making depends on interoperable data ecosystems, organizational analytics capability, governance mechanisms, explainability, and sustained human oversight. Based on these findings, this review contributes a computing-oriented decision-intelligence framework that conceptualizes analytics-enabled decision-making as an end-to-end socio-technical pipeline linking heterogeneous data acquisition, integration, feature construction, analytical modeling, explainability, human validation, governance, and feedback-based refinement. By integrating learning analytics, BI, AI, GenAI, and HITL mechanisms within a unified framework, the review clarifies how universities can move beyond dashboard-based reporting toward accountable, adaptive, and institutionally actionable decision-support infrastructures.
A Systematic Literature Review of Robustness-Aware Batik Motif Classification: Acquisition Variability, Feature Representation, and Learning Models Aji Priyambodo; R. Rizal Isnanto; Ridwan Sanjaya
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16074

Abstract

Batik motif classification has attracted growing attention in visual computing due to its role in cultural heritage preservation, textile informatics, museum documentation, and automated cataloging. Although many studies report high classification accuracy, robustness under real-world acquisition conditions remains insufficiently understood. Batik images are frequently affected by illumination variation, blur, folds, watermark overlays, wearable deformation, scale inconsistency, and background clutter, creating challenges that extend beyond conventional image-noise assumptions. Existing studies largely focus on improving classification performance, while the interactions among acquisition variability, feature representation, evaluation practice, and deployment constraints remain fragmented. This systematic literature review addresses this gap by synthesizing batik classification research through a robustness-aware perspective. Using query expansion, backward and forward citation chaining, relevance screening, and thematic coding, 116 candidate records were identified, resulting in 50 highly relevant studies for detailed analysis. The review reveals that robustness is shaped less by denoising alone than by the combined effects of acquisition conditions, representation design, evaluation realism, and deployment context. Handcrafted descriptors remain competitive for small datasets and structured motifs due to their data efficiency and interpretability, whereas deep learning models achieve the highest reported accuracy when supported by sufficient data diversity and realistic augmentation. Hybrid representations emerge as the most consistently balanced approach, combining local texture stability with higher-level abstraction across heterogeneous acquisition settings. The review further identifies recurring robustness failure patterns, including background dependency, illumination instability, motif-scale inconsistency, wearable deformation, and source-shift vulnerability. Based on these findings, a robustness-oriented research agenda is proposed, emphasizing cross-acquisition evaluation, representation-stability analysis, batik-specific robustness benchmarks, acquisition-aware augmentation, and deployable lightweight or hybrid architectures. The study contributes a domain-specific synthesis that reframes batik motif classification from an accuracy-centric task toward a robustness-aware visual recognition problem.
Co-Authors Abdul Syakur Achmad Chaerodin Achmad Hidayatno Achmad Hidayatno Ade Riyantika Dewi Adhi Susanto Adi Mora Tunggul Adi Wijaya Adian Fatchur Rochim Adrian Putranda Rispurwadi Agus Nursikuwagus Agus Suprihanto Agustini, Eka Puji Ahmad Ramdhani Aji Priyambodo Ajub Ajulian Zahra Macrina Al Iman, Yusraka Dimas Alan Prasetyo Rantelino Albert Ginting An'im Almiktad Andhika Dewanta Andhika Hanifa Naufaliawan Andino Maseleno Anton Satria Prabuwono Ardianto Eskaprianda Ari Muhardono Arianto, Mufid Aris Puji Widodo Aris Sugiharto Aris Triwiyatno Astrid Aprillini Aulia Nastiti Aziz, RZ. Abdul Bagus Hario Setiadji Basuki Rahmat Masdi Siduppa Bhutra, Yuvraj Budi Warsito Chauhan, Rahul Damar Wicaksono Danang Respati Setyabudi Deddy Sucipta Syahril Dewi, Deshinta Arrova Dewi, Rany Puspita Dhody Kurniawan Dian Kurnia Widya Buana Dian Wijayanto Dilan Arya Sujati Dimas Robby Firmanda Dini Indriyani Putri Donni Widagdo Dwi Novianto Eko Didik Widianto Eko Winarto Endang Purbowati Erizco Satya Wicaksono Ervin Adhi Cahyanugraha Fahmi Reza Ferdiansyah Fatima Setyani Ferry Dwi Setiyawan Fifiana Wisnaeni Firdaus Aditya GALIH WICAKSONO Gilang Aditya Pamungkas Handayani, Sri Hardiyanto Hardiyanto Hayu Andarwati Hefmi Fauzan Imron Hendy Cahya Lesmana Heri Purwanto Heru Prastawa Hilal Afrih Juhad Ike Pertiwi Windasari Imaduddin Amrullah Muslim Imam Tahyudin Irham Fa'idh Faiztyan Iwan Purwanto Jatmiko Endro Suseno Julianto, Dewa Rizki Rahmat Kataria, Yachi Kurnia, Dita Juni Kurniawan Teguh Martono Kurniawan, Tri Basuki Kusworo Adi Lia Lidya Roza Liga Filosa M Said Hasibuan Maizary, Ary Maman Somantri Martin Clinton Tosima Manullang Maulana Muhammad Iqbal Meidiana Dwidiyanti Misik Puspajati Nurmadjid Saputri Mona Pradipta Hardiyanti Muh. Udka Muhamad Taopik Gibran Muhammad Fahmi Awaj Muhammad Kautsar Muhammad Nur Hadi Munawar Agus Riyadi Mustafa, Mustafa Mustafid Mustafid Nahdi Saubari Nanang Sulaksono, Nanang Nazwan, Nazwan Neneng Neneng Nugroho, Waluyo Nur Setyo Permatasasi Putri W Nurhayati, Oki Dwi Nurul Arifa Oky Dwi Nurhayati Parlika, Rizky Pertiwi, Rahayu Putri Prakasa, Fawwaz Bimo Pramuko Tri Prastowo Prima Widyaningrum PUJI LESTARI Qidir Maulana Binu Soesanto Qoriani Widayati Rachel Chrysilla Tijono Refika Khoirunnisa Reza Najib Hidayat Ridwan Sanjaya Rinta Kridalukmana Rinta Kridalukmana Rivaldi MHS Riyana Putri, Fayza Nayla Rizaldi Habibie Rizaldy Khair Rizky Gelar Maliq Rosdelima Hutahaean Roza, Lia Lidya Rozak, Rofik Abdul Ruli Handrio Santoso, Imam Saptian, Fiega Adhi Sapto Nisworo Sasongko, Cornelius Damar Satya Arisena Hendrawan Setiawan, Annas Setyowati, Ro’fah Sri Lestari Sri Sumiyati Sri Widodo, Thomas Suhardjo Suhardjo Sumardi . Talitha Almira Taqwa Hariguna Teguh Dwi Prihartono Tikaningsih, Ades Toni Wijanarko Adi Putra Triloka, Joko Tyas Panorama Nan Cerah Ufan Alfianto Unaliya, Maitri Wandri Okki Saputra Wibaselppa, Anggawidia Widi Puji Atmojo Widiasmoro, Andi Wijaya, Elang Pramudya Yatim, Ardiyansyah Saad Yeh, Ming-Lang Yenita Dwi Setiyawati Yessy Kurniasari Yongki Yonatan Marbun Yudi Eko Windarto Yunus Anis, Yunus