Article Per Year (5 Year)
p-Index From 2021 - 2026
3.51
P-Index
This Author published in this journals
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 Infotek : Jurnal Informatika dan Teknologi Journal of Applied Data Sciences Jurnal Locus Penelitian dan Pengabdian 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 Automotive Engineering 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 Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Transportation

Published : 106 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 106 Documents
Search

 7   8   9   10   11 
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.
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.
Computer Vision-Based Chili Pepper Dryness Classification Using Lightweight CNN Models for Affordable Post-Harvest Sorting Tri Raharjo Yudantoro; Moh. Djaeni; R. Rizal Isnanto; Prayitno Prayitno; Z. N. Novita Sari
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 7 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i7.5975

Abstract

The manual grading of chili pepper dryness is uneven because human graders tend to perceive color, texture, and form changes that occur gradually during drying in a subjective way. The purpose of this study is to develop a lightweight convolutional neural network model that can effectively balance classification accuracy, validation stability, and deployment feasibility for inexpensive post-harvest sorting. A controlled visual dataset of 1,662 photos of red chili pepper from 32 samples at eleven drying times was gathered and classified into Fresh, Medium, and Dry classes. We assessed MobileNetV2, NASNetMobile, and InceptionV3 using the same pre-processing, augmentation, and hold-out testing protocol, along with additional robustness analysis. MobileNetV2 achieved the best hold-out performance with 93% accuracy, 93% precision, 92% recall, and 92% F1-score, while having fewer parameters and lower computational cost than NASNetMobile and InceptionV3. The class-wise analysis showed that the greatest errors were found between the Fresh–Medium and Medium–Dry boundaries, as the visual transition of chili dryness is gradual. MobileNetV2 is the most suitable baseline for low-cost camera-based chili pepper dryness sorting, and this study provides an evidence-based standard for post-harvest visual inspection using compact deep learning.
Analisis Pengenalan Pola Daun Menggunakan Metode Linear Discriminant Analysis (LDA) dan Jarak Minkowski Dian Ami Widyati; R. Rizal Isnanto; Munawar Agus Riyadi
Jurnal Transformatika Vol. 18 No. 2 (2021): January, 2021
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v18i2.2975

Abstract

Indonesia adalah negara tropis yang memiliki keanekaragaman jenis tumbuhan. Tumbuhan terdiri atas tiga organ dasar yaitu akar, batang dan daun. Daun merupakan salah satu bagian yang sering digunakan untuk mengklasifikasikan tanaman, karena setiap jenis tanaman memiliki ciri yang berbeda. Bentuk tepian daun bisa digunakan untuk acuan klasifikasi daun. Otak manusia memiliki keterbatasan dalam mengolah atau mengignat informasi jenis-jenis tanaman yang berdasarkan daun. Oleh karena itu dibutuhkan peralihan pengetahuan manual ke suatu sistem digital. Maka dalam penelitian ini dibuat sistem yang mampu melakukan pengenalan daun menggunakan ekstraksi ciri pada daun menggunakan metide Linear Discriminant Analysis ( LDA ) dan jarak Minkowski.Proses pengenalan pola citra daun diawali dengan pengambilan citra daun, kemudian masuk ke tahap prapengolahan untuk membedakan objek dengan latar belakang. Setelah itu masuk ke tahap ekstraksi ciri menggunakan Linear Discriminant Analysis (LDA ) untuk mendapatkan karakteristik ciri dari citra dan Jarak Minkowski untuk melakukan pengenalan dari pola daun.                       Berdasarkan hasil penelitian dengan jumlah data sebanyak 40 kelas dengan masing-masing kelas sebanyak 6 citra, dengan citra latih sebanyak 160 citra daun dan citra uji sebanyak 80 citra daun. Saat pengenalan menggunakan jarak minkowski menggunakan 3 koefisien yaitu koefisien minkowski 1, 2, dan 3. Dari ­ ­masing-masing koefisien minkowski didapatkan persentase keakurasian. Persentasi keakurasian saat menggunakan koefisien minkowski 1 sebesar 41,25%, koefisien minkowski 2 sebesar 33,75%, dan koefisien minkowski 3 sebesar 30%. Persentase keakurasian pada penelitian ini tidak dapat menghasilkan diangka 80% dikarenakan jumlah data sangat mempengaruhi hasil persentase, semakin banyak data yang ada maka nilai persentase juga akan semakin kecil.
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 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 Ami Widyati Dian Kurnia Widya Buana Dilan Arya Sujati Dimas Robby Firmanda Dini Indriyani Putri Donni Widagdo Dwi Novianto Eko Didik Widianto Eko Winarto Erizco Satya Wicaksono Ervin Adhi Cahyanugraha Fahmi Reza Ferdiansyah Fatima Setyani Ferry Dwi Setiyawan Firdaus Aditya GALIH WICAKSONO Gilang Aditya Pamungkas Handayani, Sri Hardiyanto Hardiyanto Hayu Andarwati Hefmi Fauzan Imron Hendy Cahya Lesmana Heri Purwanto Hilal Afrih Juhad Ike Pertiwi Windasari Imaduddin Amrullah Muslim Imam Tahyudin Irham Fa'idh Faiztyan Jatmiko Endro Suseno Julianto, Dewa Rizki Rahmat Kataria, Yachi 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 Misik Puspajati Nurmadjid Saputri Moh. Djaeni 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 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 Prayitno 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 Sri Lestari Sri Sumiyati Sri Widodo, Thomas Suhardjo Suhardjo Sumardi . Talitha Almira Taqwa Hariguna Teguh Dwi Prihartono Tikaningsih, Ades Toni Wijanarko Adi Putra Tri Raharjo Yudantoro 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 Yunus Anis, Yunus Z. N. Novita Sari