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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknik Industri J@TI (TEKNIK INDUSTRI) Jurnal Rekavasi SITEKIN: Jurnal Sains, Teknologi dan Industri JURNAL PASTI (PENELITIAN DAN APLIKASI SISTEM DAN TEKNIK INDUSTRI) Jurnal Optimasi Sistem Industri Jurnal Ilmiah Teknik Industri JOURNAL OF INDUSTRIAL AND MANUFACTURE ENGINEERING Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Jurnal Teknik Industri : Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri Jurnal Mutu Pangan : Indonesian Journal of Food Quality International Journal of Advances in Data and Information Systems Jurnal Media Teknik dan Sistem Industri Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Optimasi Teknik Industri (JOTI) Industrika : Jurnal Ilmiah Teknik Industri Go-Integratif : Jurnal Teknik Sistem dan Industri Green Intelligent Systems and Applications Journal of Industrial Engineering and Halal Industries Spektrum Industri
Achmad Pratama Rifai
Department Of Mechanical And Industrial Engineering, Universitas Gadjah Mada
Religion Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Transportation Other

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Penerapan Algoritma Simulated Annealing dan Large Neighborhood Search pada Vehicle Routing Problem with Simultaneous Pickup and Delivery Di PT Pos Indonesia Yogyakarta Susilo, Nazhifa Rahmi; Thawafani, Lathiifah; Buchari, Muhammad Achirudin; Valencia, Bella Renata; Rifai, Achmad Pratama
Jurnal Optimasi Teknik Industri (JOTI) Vol 6, No 2 (2024)
Publisher : Teknik Industri Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/joti.v6i2.24821

Abstract

Pengembangan Sistem Pendukung Keputusan Untuk Prediksi Diabetes Aldyno, Achmad Farhan; Junaidi, Faiza Ulinnuha; Rabbani, Haidar; Oda, Ahlam Nauf; Rifai, Achmad Pratama
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 2 (2024): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i2.787

Abstract

Diabetes is one of the major health issues worldwide, affecting 10.5% of the total adult population (20-79 years old). Often referred to as the silent killer, nearly half of those affected by diabetes are unaware of their condition. Diabetes is categorized into several types, namely type 1 diabetes mellitus, type 2 diabetes mellitus, and gestational diabetes. Detection of diabetes can be carried out through various methods, including blood sugar level tests, Hemoglobin A1c (HbA1c) tests, oral glucose tolerance tests, as well as physical examinations and medical history reviews by doctors. Interpreting the results of these tests can be used to identify the potential for an individual to have diabetes, employing a machine learning approach as a decision support system for doctors to make informed decisions, and also providing patients with reminders to consult with a doctor. In the machine learning model we've developed, we trained and tested algorithms using the 'Diabetes prediction dataset,' consisting of 8 variables: age, gender, Body Mass Index (BMI), hypertension, heart disease, smoking history, HbA1c level, and blood glucose level. The algorithm employed was the Artificial Neural Network (ANN) with the optimizer using Stochastic Gradient Descent (SGD). This application is intended to serve as a decision support system for doctors and the general public. It's designed using Anvil for 8 types of input variables, providing 2 output variables: the percentage of an individual's potential to have diabetes and suggestions for preventing such risks.
Prediction of Anime Rating with Hybrid Artificial Neural Networks and Convolutional Neural Networks Al Kautsar, M. Nurudduja; Anggraini, Violita; Basirun, Arif Reza; Rifai, Achmad Pratama
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 22, No 1 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v22i1.28390

Abstract

This study proposes an innovative approach to predict anime scores by leveraging a combination of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). Tabular data such as source, number of episodes, type, and genre are incorporated alongside the image representation of anime into a holistic model. Evaluation results on the test set show satisfactory performance, with an average loss value of 0.673, Mean Absolute Error (MAE) of 0.654, and Mean Absolute Percentage Error (MAPE) of 9.44%. Training and validation graphs reflect the model's convergence without significant signs of overfitting or underfitting. The integration of information from both data sources yields a model capable of providing accurate predictions of anime scores, contributing to an understanding of trends and preferences in the anime industry, and opening opportunities for the development of similar models in the field of score prediction or other quality evaluations.
Artificial Neural Network for Benchmarking the Dimensional Accuracy of the PLA Fused Flament Fabrication Process Setiawan, Kevin Stephen; Tanaji, Irvantara Pradmaputra; Permana, Ari; Akbar, Hafizh Naufaly; Prihatmaja, Dhonadio Aurell Azhar; Normasari, Nur Mayke Eka; Rifai, Achmad Pratama; Pamungkasari, Panca Dewi
Green Intelligent Systems and Applications Volume 4 - Issue 2 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v4i2.522

Abstract

Fused Deposition Modeling (FDM) is an additive manufacturing technique that uses a 3D printer to extrude molten filament through a nozzle, which moves along the X, Y, and Z axes to create parts with the desired geometry. FDM offers numerous advantages, especially for producing parts with complex shapes, due to its ability to enable rapid and cost-effective manufacturing compared to traditional methods. This study implemented an Artificial Neural Network (ANN) to optimize process parameters aimed at minimizing dimensional inaccuracies in the FDM process. Key parameters considered for optimization included the number of shells, infill percentage, and nozzle temperature. The ANN utilized three algorithms: Scaled Conjugate Gradient, Bayesian Regularization, and Levenberg-Marquardt. Model performance was evaluated based on dimensional deviations along the X and Y axes, with a hidden layer of 25 neurons. Among the algorithms, Scaled Conjugate Gradient provided the most accurate results in minimizing dimensional errors.
Perancangan Ulang Tata Letak IKM Tahu Sehat Sari untuk Mengurangi Jarak Material Handling Fadilah, Andara; Hikam , Azka; Muhtar , Dini; Anom , Mauli; Rifai, Achmad Pratama
Industrika : Jurnal Ilmiah Teknik Industri Vol. 9 No. 1 (2025): Industrika: Jurnal Ilmiah Teknik Industri
Publisher : Fakultas Teknik Universitas Tulang Bawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37090/indstrk.v9i1.1831

Abstract

Small and Medium Enterprises (SMEs) typically operate within limited or small spaces, with production activities that are sometimes mismatched with their available conditions. This includes the Tahu Sehat Sari SME case, where the existing product layout results in significant material handling movement, leading to high operational costs. This research designs a facility layout tailored to the SME’s needs. The layout is developed considering various constraints such as cost, available area, and other resources. The goal of this design is to create a new production layout for Tahu Sehat Sari SME, accommodate its relatively high daily production capacity, to minimize operational costs, reduce material handling distances, and increase productivity. Based on the initial layout of the SME, proposed layouts using methods such as Modified Spanning Tree (MST) and Simulated Annealing (SA) are compared. From the layout designs using single-row Simulated Annealing, double-row Simulated Annealing, and Modified Spanning Tree, the total material handling distances calculated are 8,590, 1,950, and 1,695, respectively, with the total material handling distances in the current layout 2,600 m. The most suitable layout design is determined based on the minimization of total travel distance, directly correlating to the optimization of material handling and the elimination of waste. Keywords: Double Row, Layout Planning, Modified Spanning Tree, SimulatedAnnealing, Single Row
Twitter Sentiment Analysis of Mental Health Issues Post COVID-19 Pamungkasari, Panca Dewi; Ningsih, Sari; Rifai, Achmad Pratama; Nandila, Alisyafira Sayyidina; Nguyen, Huu Tho; Penchala, Sathish Kumar
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.588

Abstract

The Coronavirus Disease 2019 (COVID-19) impacted many aspects of daily life, including mental health, as some individuals struggled to adjust to the rapid changes brought on by the pandemic. This paper investigated sentiment analysis of Twitter data following the COVID-19 pandemic. Specifically, we analyzed a large corpus of tweets to understand public sentiment and its implications for mental health in the post-pandemic context. The Naïve Bayes and Support Vector Machine (SVM) classifiers were used to categorize tweets into positive, negative, and neutral sentiments. The collected tweet data samples showed that 38.35% were neutral, 32.56% were positive, and 29.09% were negative. Results using the SVM method showed an accuracy of 84%, while Naïve Bayes achieved 80% accuracy.
Comparison Of Feature Extraction Techniques For Long Short-Term Memory Models In Indonesian Automatic Speech Recognition Armaisya, Dimas Dwi; Pamungkasari, Panca Dewi; Rifai, Achmad Pratama; Sholihati, Ira Diana; Gopal Sakarkar
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.605

Abstract

Automatic Speech Recognition (ASR) faced challenges in accuracy and noise robustness, particularly in Bahasa Indonesia. This research addressed the limitations of single feature extraction methods, such as Mel-Frequency Cepstral Coefficients (MFCC), which were sensitive to noise, and Relative Spectral Transform - Perceptual Linear Predictive (RASTA-PLP), which was less effective in frequency representation, by proposing a hybrid approach that combined both techniques using Long Short-Term Memory (LSTM) models. MFCC enhanced spectral accuracy, while RASTA-PLP improved noise robustness, resulting in a more adaptive and informative acoustic representation. The evaluation demonstrated that the hybrid method outperformed single and non-extraction approaches, achieving a Character Error Rate (CER) of 0.5245 on clean data and 0.8811 on noisy data, as well as a Word Error Rate (WER) of 0.9229 on clean data and 1.0015 on noisy data. Although the hybrid approach required longer training times and higher memory usage, it remained stable and effective in reducing transcription errors. These findings suggested that the hybrid method was an optimal solution for Indonesian speech recognition in various acoustic conditions.
Optimasi Open Location Routing Problem Menggunakan Metode Metaheuristik Simulated Annealing, Large Neighborhood Search, dan Adaptive Large Neighborhood Search Muhammad, Audi Ziyad Afkar; Kafi, Mochamad Egidio Pramudya; Hasibuan, Narsico Rafael; Rahmawatie, Noor Athiea; Rifai, Achmad Pratama
Jurnal Optimasi Teknik Industri (JOTI) Vol 7, No 1 (2025)
Publisher : Teknik Industri Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/joti.v7i1.24873

Abstract

Overcoming Data Imbalance in Risk Management: A Comparative Study of Sampling Methods Astungkara, Arya Wijna; Rifai, Achmad Pratama
JTI: Jurnal Teknik Industri Vol 11, No 1 (2025): JUNI 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jti.v11i1.37368

Abstract

Data imbalance is a significant challenge in risk management, especially in classification tasks where critical events—such as loan defaults, employee attrition, or company bankruptcy—occur less frequently than normal cases. This paper presents a comparative study of eight sampling methods—Random Undersampling (RUS), Random Oversampling (ROS), Edited Nearest Neighbor (ENN), One-Sided Selection (OSS), SMOTE, ADASYN, SMOTEENN, and SMOTETomek—across three imbalanced datasets: Taiwanese Bankruptcy Prediction, IBM HR Analytics Employee Attrition, and Loan Prediction. Using eight machine learning classifiers, the study evaluates performance using F1 Score and Negative Predictive Value (NPV), two metrics suited for imbalanced data. The results reveal that ENN achieves the highest F1 scores in high-dimensional and severely imbalanced datasets, while SMOTE-based methods perform best in large-scale datasets with moderate imbalance. Notably, RUS consistently delivers the highest NPV, highlighting its effectiveness in minimizing false negatives and supporting conservative decision-making. The findings underscore the importance of aligning sampling strategies with dataset characteristics and specific risk management objectives.
Comparative Evaluation of Convolutional Neural Network Full Learning Model with Transfer Learning (VGG-16) for Coffee Bean Roasting Level Classification Tama, Mradipta Nindya; Saptomo, Amanat Bintang; Afrido; Baroroh, Dawi Karomati; Rifai, Achmad Pratama; Tho, Nguyen Huu
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1358

Abstract

Indonesia is the 3rd largest coffee producing country in the world in 2022-2023 with coffee production reaching 11.85 million bags per 60 kg of coffee. One of the important processes in coffee production is roasting because the roasting level of coffee beans can affect the taste and aroma of coffee. The problem faced is that the process of assessing the level of coffee roasting is traditionally carried out through visual observation by an expert (roaster). This method produces a subjective level of assessment and requires high skills and experience, making the assessment of the level of coffee roasting less efficient and prone to human error. Therefore, in this study the author aims to develop a Convolutional Neural Network (CNN) model for the classification of the level of coffee bean roasting that can achieve better and faster accuracy. In this study, the author compared two CNN architecture approaches for the classification of the level of coffee bean roasting. The first approach is full learning with an architecture consisting of three convolution layers. The second approach is transfer learning based on the VGG-16 model. From the results of the analysis, it is known that the full learning model has a better level of accuracy and a faster running time than the VGG-16 transfer learning. The CNN full learning model for coffee bean roasting level classification is able to classify the coffee bean roasting level, with an accuracy of 98.75% and a running time of 856 ms per step. The application of CNN for coffee roasting level classification can provide benefits such as improving quality control and reducing the level of subjectivity of a roaster in assessing the roasting level of coffee beans.
Co-Authors Afrido Ainayyah Bintang Agista Akbar, Hafizh Naufaly Al Kautsar, M. Nurudduja Albab, Disya Amalia Ikhsani Ulil Aldyno, Achmad Farhan Alfarasyied Syahrizad Amirah Meutia Noorfadila Ananta, Vhysnu Satya Andiny Trie Oktavia Andri Nasution Anom , Mauli Ardyaksa Diptya Pramudita Arista Adriani Armaisya, Dimas Dwi Arulloh Sonja Asa Pragasel Natuna Asfandima, Ilhan Alim Astungkara, Arya Wijna Astungkatara, Arya Wijna Awal, Syifa Maulvi Zainun Azim, Ahmad Fadhil Basirun, Arif Reza Briliananda, Silvyaniza Buchari, Muhammad Achirudin Dawi Karomati Baroroh Devita Ayuni Kusumaningsih Evan Alvaro Radeva Fadilah, Andara Fahreza Baskara Hediandra Fath, Hamzah Fatiha Widyanti Fauzi, Rifqi Fransisca Astri Dianswari Gopal Sakarkar Hajad, Makbul Hans Bastian Wangsa Hans, Feishal Rey Hartanti, Sri Hasibuan, Narsico Rafael Hideki Aoyama Hikam , Azka Huu Tho, Nguyen Ihsan Ramadhana Jordiva Fernanda Junaidi, Faiza Ulinnuha Kafi, Mochamad Egidio Pramudya Khania O.P.P. Nugraha Korin, Filbert Kusumaningsih, Devita Ayuni Kusumastuti, Putri Adriani Ludwika, Adinda Sekar Manalu, Haposan Vincentius Mohamad, Rakan Raihan Ali Muchammad Ismail Muhammad, Audi Ziyad Afkar Muhtar , Dini Nandila, Alisyafira Sayyidina Naufal Nur Akmal Nguyen , Huu Tho Nguyen, Huu Tho Nguyen, Huu-Tho Nur Mayke Eka Normasari Nurraudah, Restu Oda, Ahlam Nauf Oktavia, Andiny Trie Pamungkasari, Panca Dewi Penchala, Sathish Kumar Permana, Ari Pohan, Rafi Naufal Al Mochtari Pratama, Dhika Wahyu Priansyah, Adi Prihatmaja, Dhonadio Aurell Azhar Puspadewa, Paskalis Krisna Puspitasari, Afifa Putra, Dimas Zaki Alkani Putri, Oktaviana Rabbani, Haidar Rachmadi Norcahyo Radhitya Virya Paramasuri Sunarso Rahmawatie, Noor Athiea Safira, Aretha Safitri, Tari Hardiani Saifurrahman, Anas Saptomo, Amanat Bintang Sari Ningsih Sari, Dwi Kumala Sarudi As., L. M. Setiawan, Kevin Stephen Setyo Tri Windras Mara Shalehah, Mar’atus Sholihati, Ira Diana Susilo, Nazhifa Rahmi Sutoyo, Edi Syahdan Haris Abdilah Tama, Mradipta Nindya Tanaji, Irvantara Pradmaputra Thawafani, Lathiifah Tho, Nguyen Huu Valencia, Bella Renata Violita Anggraini Wangi Pandan Sari Wibisono, Ragil Aditya Windras Mara, Setyo Tri Wiraningrum, Rakyan Galuh Yana, Anak Agung Istri Anindita Nanda