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ZK Abdurahman Baizal
Universitas Telkom
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Diet and Physical Exercise Recommendation System Using a Combination of K-Means and Random Forest Muhammad Ilham Hafizha; Z. K. A. Baizal
Indonesian Journal on Computing (Indo-JC) Vol. 9 No. 2 (2024): August, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.2.959

Abstract

Public health has become a significant focus in this modern era due to the increasing number of people suffering from various diseases. Unhealthy diets and lack of physical activity are often associated with multiple health problems, one of which is obesity. Several studies have been conducted to develop food recommendation systems for individuals with obesity, using K-Means and Random Forest algorithms to provide food recommendations based on user-specific aspects. However, these studies do not provide supporting information, such as physical activity recommendations to address fitness issues or lack of physical activity. This study develops a diet and physical exercise recommendation system for individuals with obesity using a combination of K-Means and Random Forest. The system categorizes and classifies foods and physical exercises and provides customized recommendations based on user data analysis. The accuracy of the system was evaluated using the MAPE metric, with the highest accuracy for dietary food recommendations being 99.03% for the non-vegan lunch diet meal recommendation and the lowest being 70.74% for the vegan morning meal diet recommendation. The MAPE for physical exercise recommendations was consistently at 26.35%, indicating a stable accuracy of 73.65%. The test results show that the system recommends diet and physical exercise accurately.
Ontology-based Conversational Recommender System for Smartwatches Thoriq Akhdan, Muh; Baizal, Z. K. A.
Journal of Information System Research (JOSH) Vol 5 No 2 (2024): Januari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i2.4784

Abstract

In recent years, smartwatches have become popular in the mobile technology market. However, with various smartwatch models and brands available, prospective buyers often need help choosing the right product due to specifications that require technical understanding and expert opinions. Therefore, a recommender system is needed to assist prospective buyers in choosing the appropriate product. Several studies have been conducted on conversational recommender systems. However, the recommender systems used only provide recommendations based on technical specifications alone, so the recommendations given are less personalized. Therefore, we develop a conversational recommender system for smartwatches using ontology that considers the functional needs of users to produce customized recommendations. In this study, we have successfully built and evaluated this system using recommendation accuracy metrics and user satisfaction. The evaluation results show an accuracy of 86.67% and positive user feedback. This indicates that our system is accurate, easy to use, and well-accepted.
Car Recommender System Using Collaborative Filtering and Ontology-Based Conversational Recommender System Radhiva Hibatullah, Muhammad; Baizal, Z. K. A.
Journal of Information System Research (JOSH) Vol 5 No 2 (2024): Januari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i2.4785

Abstract

The development of the automotive industry in Indonesia is increasing, especially in automobiles. Due to the increasing number of car brands in Indonesia, it is difficult for users to decide which car suits their functional requirements. Therefore, to overcome this problem, we propose a ontology-based Conversational Recommender System (CRS) using Collaborative Filtering. CRS as a framework aims to have users interact with the system so that the system obtains information related to users functional requirements, ontology-based aims to organize domain knowledge with specific concepts, and Collaborative Filtering improve the accuracy of recommender products in developing recommender systems. The evaluation results include system performance with 85.39% accuracy and user satisfaction getting positive feedback from various factors. This shows that the car recommender system is effective and efficient in providing recommendations according to the functional requirements of users.
Personalized Ontology-based Food Menu Recommender System for Bodybuilders using SWRL Rules Hakim, Lukman Nur; Baizal, Z. K. A.
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5755

Abstract

Bodybuilding requires precise and careful food planning to promote muscle growth and optimize body composition. However, creating personalized meal plans that meet the unique dietary needs of bodybuilders is challenging. This study introduces a customized food recommender system specifically designed for bodybuilders, addressing this problem by utilizing an ontology-based approach combined with Semantic Web Rule Language (SWRL) and a Telegram chatbot. The objective is to provide personalized nutritional guidance that aligns with individual bodybuilding goals. The system employs ontologies to represent key concepts such as user profiles, nutritional needs, and food attributes. SWRL rules generate tailored meal plans based on the user's input, which includes personal information and bodybuilding objectives submitted through the chatbot. The system was evaluated with 15 user profiles, producing 180 food recommendations. The results demonstrated high accuracy, with a precision value of 0.866, a recall value of 1, and an F-Score of 0.928. Although the system effectively delivers personalized nutritional advice, it currently lacks the ability to address specific dietary restrictions. Future work could involve incorporating a wider range of dietary considerations and enhancing the system's applicability. This study highlights the potential of semantic technologies in advancing personalized diet and fitness planning.
An Approach to a Group Movie Recommender System using Matrix Factorization-based Collaborative Filtering Darmawan, Faiha Adzra; Baizal, Z. K. A.
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1126

Abstract

The growth of online movie streaming platforms has driven the demand for recommender systems that are able to deal with the daunting challenge of users finding movies that match their preferences. However, these recommender systems tend to focus on the needs of individual users, whereas in the real world, there are circumstances in which recommendations are needed for a group of users. Therefore, this study proposes a Group Recommender Systems (GRS) using Matrix Factorization (MF) with aggregation model to recommend movies for a group of users. We employ three Matrix Factorization methods to three distinct group sizes, which are small, medium, and large. Our goal is to identify the most effective approach for each group size. To evaluate the performance, we use precision and recall as measurement metrics. The results show that the MF method, After Factorization (AF) outperforms the other MF methods, i.e., Before Factorization (BF) and Weighted Bfore Factorization (WBF) in terms of precision parameters for small groups (2-4 users), which achieving a score of 0.86. Meanwhile, BF method surpassing both AF and WBF in precision parameters for medium groups (5-8 users) with a score of 0.81.
PENGGUNAAN TEKNIK FEATURE WEIGHTING UNTUK PEMBERSIHAN NOISE PADA HALAMAN SITUS BERITA BERBAHASA INDONESIA Rahmat Firdaus; Abdurahman Baizal; Yanuar Firdaus A
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 8, No 1, Januari 2010
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (928.409 KB) | DOI: 10.12962/j24068535.v8i1.a71

Abstract

A web page usually consists of information in every page blocks displayed. In some cases, news content displayed in a news website are not entirely relevant or are unrelated to the main content such as navigation panel, copyright, user guide, links, news summary, various advertisement etc. Information blocks irrelevant to the main content is known as web pages noise. This research applies feature weighting technique to improve classification results by detecting a noise in pages of a website. Using feature weighting technique the web is first modelled with Document Object Model(DOM) tree and Compressed Structure Tree(CST) to obtain the general structure and compare the information blocks in awebsite.Information obtained is used to measure and evaluate the importance level of each node created by Compressed Structureed Tree(CST). Based on the tree created and the importance level of each node, this method assign weights on each individual word (feature) in each content block. The weights will be used in web mining process.
PERPADUAN COMBINED SAMPLING DAN ENSEMBLE OF SUPPORT VECTOR MACHINE (ENSVM) UNTUK MENANGANI KASUS CHURN PREDICTION PERUSAHAAN TELEKOMUNIKASI Fernandy Marbun; Abdurahman Baizal; Moch Arif Bijaksana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 8, No 2, Juli 2010
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (11932.256 KB) | DOI: 10.12962/j24068535.v8i2.a316

Abstract

Churn prediction adalah suatu cara untuk memprediksi pelanggan yang berpotensial untuk churn. Data mining khususnya klasifikasi tampaknya dapat menjadi alternatif solusi dalam membuat model churn prediction yang akurat. Namun hasil klasifikasi menjadi tidak akurat disebabkan karena data churn bersifat imbalance. Kelas data menjadi tidak stabil karena data akan lebih condong ke bagian data yang memiliki komposisi data yang lebih besar. Salah satu cara untuk menangani permasalahan ini adalah dengan memodifikasi dataset yang digunakan atau yang lebih dikenal dengan metode resampling. Teknik resampling ini meliputi over-sampling, under-sampling, dan combined-sampling. Metode Ensemble of SVM (EnSVM) diharapkan dapat meminimalisir kesalahan klasifikasi kelas mayor dan minor yang dihasilkan oleh classifier SVM tunggal. Dalam penelitian ini akan dicoba untuk memadukan combined sampling dan EnSVM untuk churn predicition. Pengujian dilakukan dengan membandingkan hasil klasifikasi CombinedSampling-EnSVM dengan SMOTE-SVM (perpaduan oversamping-SVM) dan pure-SVM. Hasil pengujian menunjukkan bahwa metode CombinedSampling-EnSVM secara umum hanya mampu menghasilkan performansi Gini Index yang lebih baik daripada metode SMOTE-SVM dan tanpa resampling (pure-SVM).
Menjembatani Teknologi dan Spiritualitas: Pengenalan Artificial Intelligence di Pondok Pesantren melalui Workshop Kolaboratif Mahmud Dwi Sulistiyo; Didit Adytia; Z.K. Abdurahman Baizal; Raihani Mohamed; Nabila Wardah Zamani; Nurfadhlina Mohd Sharef
I-Com: Indonesian Community Journal Vol 5 No 3 (2025): I-Com: Indonesian Community Journal (September 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i3.7799

Abstract

Pesantren sebagai lembaga pendidikan berbasis keagamaan memiliki tantangan dalam mengakses perkembangan teknologi terbaru seperti Artificial Intelligence (AI). Menjawab kebutuhan ini, tim pengabdian dari Telkom University menyelenggarakan workshop pengenalan AI dan mengundang sivitas Pondok Pesantren Modern Assuruur. Kegiatan ini bertujuan untuk meningkatkan literasi digital para santri melalui pengenalan konsep dasar AI dan pemanfaatannya dalam pembelajaran Al-Qur’an. Workshop dilakukan secara hybrid bersama narasumber dari Telkom University dan Universiti Putra Malaysia (UPM), mencakup pretest, materi inti, praktik aplikasi Tarteel, dan posttest. Hasil evaluasi menunjukkan peningkatan skor pemahaman peserta dari rata-rata 93,97 menjadi 98,10, disertai penurunan standar deviasi dari 14,50 menjadi 5,88. Kegiatan ini membuktikan bahwa pendekatan yang kontekstual dan aplikatif mampu meningkatkan pemahaman santri terhadap teknologi modern serta membuka peluang integrasi AI dalam pendidikan Islam.
Recommender System for Group of Users using Matrix Factorization for Tourism Domain (Case Study: Bali) Ruh Devita Widhiana Prabowo; Z. K. A. Baizal
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1129

Abstract

Choosing a product that suits a customer's needs requires a recommendation system to provide suggestions on a collection of items of interest to the user. Recommendations can be applied in various fields such as entertainment, shopping sites, social networking, job portals, discovery of relevant web pages, and so on. There are many circumstances where recommendations are needed for a group such as in tourism and entertainment purposes. The development of a Group Recommendation System (GRS) was carried out in response to the need to provide several recommendations to a group of users. We conducted this research to build a GRS that can provide item recommendations using the Collaborative Filtering (CF) method with Matrix Factorization Technique, as well as three approaches, namely After Factorization (AF), Before Factorization (BF), and Weighted Before Factorization (WBF). Determine the best approach for the three categories of groups formed, namely small groups (three members), medium groups (five members), and large groups (ten members). The focus of this research is the tourism destination domain in Bali. In the evaluation results of the precision calculation, the medium group obtained the highest score for the AF, BF, and WBF approaches of 0.944. Meanwhile, in calculating recall, the small group achieved the highest scores for the AF, BF, and WBF approaches of 0.294, 0.259, and 0.259, respectively. From the results of this study, it appears that small groups are suitable for using the BF approach, while the AF method is more effective for large groups, and the best approach for medium groups is the WBF.
Collaborative filtering-based group recommender system using sparse autoencoder Bahar, Musthafa Zaki; Baizal, Zinke Abdurahman
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.1702

Abstract

The development of technology makes the distribution of information easier and faster, but leads to information overload. A recommender system is one tool to overcome information overload, while the collaborative filtering (CF) paradigm is a widely used approach in recommender systems. The recommender system generally focuses on individual recommendations, but in real conditions, recommendations for a group are often needed, for example, when we want to listen to music with friends, or we plan a vacation with family. Many prior studies have used the CF paradigm with matrix factorization to build group recommender systems. Matrix factorization has been shown to alleviate the sparsity problem; however, it does not fully resolve it. Therefore, we propose an approach that uses a sparse autoencoder to address this sparsity issue. We chose the sparse autoencoder because it can effectively capture latent patterns in sparse data by learning a compressed representation while retaining important features crucial for accurate recommendations. We built a group recommender system with three different group sizes and aggregation approaches. For evaluation, we use the root-mean-square error (RMSE) and the mean absolute error (MAE). Test results indicate that the sparse autoencoder outperforms matrix factorization in terms of RMSE and MAE. This study improves group recommender systems by addressing data sparsity using a sparse autoencoder. The proposed approach enhances recommendation accuracy compared to traditional matrix factorization methods.
Co-Authors Abdul Muqit Abdullah Helmy Ade Kosasih Ade Romadhon Ade Sukma Adisti Rastosari Aditya, Naufal Adri Nur Fajari Afriani Sandra Agung Toto Wibowo Agus Alim Abdullah Ahmad Lubis Ghozali Akbar, Yoan Amri Alam Rahmatulloh Albi Fitransyah Ali, Muhammad Haidir Allismawita Allismawita amnah amnah An Fauzia Rozani Syafei Ana Fitriana Poerana Andiety, Rich Andini, Andini Andjioe, Oscar Rynandi Angelina Sagita Sastrawan Anindya, Widya Dara Aniq A Rahmawati Aniq A. Rahmawati Anisa Herdiani Annisa Cahya Anggraeni Annisa Cahya Anggraeni Antonius Randy Arjun Ardi Ardi Ari Satrio Arie Lasaprima Arifa Nur Hasanah Aryadi Pramarta Ayunda Farah Istiqamah Bahar, Musthafa Zaki Budiarti, L Endang Burhanuddin Bahar Cahya, Anindya Cahyani, Hilda Canda Ayu Arum Pertiwi Christhofer Laurent Juliant Cut Sri Maulina D. Novia Daffa Barin Tizard Riyadi Damayanti, Elok Dana Sulistyo Kusumo Danang Triantoro Murdiansyah Darmawan, Faiha Adzra Dede Tarwidi Dedi Romli Triputra Dendy Andra Deni Novia Dessy Abdullah Devi Pratami Devina Vanesa Dhiva Rezzy Pratama Diah Mahmuda Diah Pudi Langgeni Didit Adytia Djoko Wahyono Donni Richasdy Dreyfus, Shoshana Dwi H Widyantoro Dwi Maya Sari Dwinda Tamara Edy Tandililing Eka Ismantohadi Elly Roza Elsa Rachel Dementieva Erbina Selvia Br Perangin-Angin Erliansyah Nasution Erni Masdupi Erwin B. Setiawan Erwin Budi Setiawan Esa Alfitrassalam Evitayani Evitayani Fadillah, Ichsan Alam Fatimah Nurhayani Fatimatus Zahroh Favian Dewanta Ferawati Ferawati Fernandy Marbun Ferry Lismanto Syaiful Firmansyah Firmansyah Fitriani Mangerangi Gentra Aditya Putra Ruswanda Gesit Tabrani Ghazi Ahmad Fadhlullah Gholib Gholib Grace Yohana Grace Yohana Gusti Ayu Marheni Gustina Lubis Hafid Ahmad Adyatma Hakim, Lukman Nur Hary Yuswadi Hasanuddin Hasanuddin Hasanusi, Mohammad Helmi Arifin Hendra Naldi Hendri Andi Mesta Humaizi, Humaizi Humaizi, Humaizi Ichwanul Muslim Karo Karo Ida Ayu Putu Sri Widnyani Igga Febrian Virgiani Ika Arum Puspita Ilham Mujaddid Al Masyriq Imam Sunarno, Imam Ina Rofi’atun Nasihati Indira Adnani Indri Juliyarsi Inggrid Resmi Benita Intan Dwi Novieta, Intan Dwi Irfan Darmawan Irhas Jaya Iryanto Iryanto Iut Tri Utami Izzatul Ummah Jaka E. Sembodo Jamhari Jamhari Jamsari Jamsari Jaya, Irhas Jayana Citra Agung Pramu Putra Joni Dwi Pribadi Kalsum Kalsum Kemas M Lhaksmana Kemas M. Lhaksmana Kemas Muslim Lhaksmana Khaidarmansyah Khairiah, Khairiah Khamim, Khamim Khasrad . Khatimah, Ummu Husnul Khoirunnisaa’ Khoirunnisaa’ Khusnul Diana Kun Mustain Kusnadi, Kusnadi Lie Othman Lilis Suryani Lisa Rahmi Litasari Widyastuti, Litasari Liviandra, Monica Loiz, Andhika Lubis, Putri Handayani Lutfi Ambarwati M. Duskri M. Naufal Mu'afa M. Qadrian M. Rayhan Hakim Mahmud Dwi Sulistiyo Mahmud Imrona Mala Nurilmala Mansyur Arif Marayasa, I Nyoman Marendra Septianta Mayasari Mayasari Mella Ismelina F. Rahayu Miranti Andhita Scantya Mirna Fitrani Misna Ariani Mizanul Kirom Moch Arif Bijaksana Moh Naufal Mizan Saputro Moh Z Mubarok Moh. Mahsus Mudayatiningsih, Sri Muhamad Faishal Irawan Muhamad Hafidh Nofal Muhammad Adlim Muhammad Agus Muljanto Muhammad Alwi Nugraha Muhammad Attalariq Muhammad Bilal Rafif Azaki Muhammad Ilham Hafizha Muhammad Ilham Hafizha Muhammad Ridha Anshari Muhammad Zaid Dzulfikar Mustakim, ' Mustofa, Mutmainnah Mutmainnah Mustofa Nabila Wardah Zamani Najla Nur Adila Naufal Akbar Hartono Ni Nyoman Sumiasih Ni Wayan Armini Niken Titi Pratitis Ningsih Purba Ningsih, Ayu Oktavia Nirmala Ayu Aryanti Nisa, Intan Khairu Nofal, Muhamad Hafidh Nora. AN, Desri Nungki Selviandro Nur Azlina Nur Jamilah Nur Rahmawati Nur Ulfa Maulidevi Nuraini Lubis Nurfadhlina Mohd Sharef Nurjayanto, Bagus Wicaksono Nurul Ikhsan Okky Brillian Hibrianto Okky Brillian Hibrianto P, Kadek Abi Satria A V Pahrurrobi Pahrurrobi Paskalis Aditya Putra Prasetia, Reza Putra, A. D. A. Putu Harry Gunawan Qisti R Arvianti Rachmi Helfianur Radhiva Hibatullah, Muhammad Rafiuddin, Rafiuddin Rahmat Firdaus Rahmi Wati Raihani Mohamed Rais Rais Ramadhan, Sageri Fikri Ramadhani, Nur Laili Ramanti Dharayani Randika Dwi Maulana Rasyid Ranestari Sastriani Rasbawati, Rasbawati Rayhan M Auliarahman Reinaldo Kenneth Darmawan Rena Feri Wijayanti Restu Aditya Rachman Reza Rendian Septiawan Rezano, Tomi Richo Fedhia Saldhi Rika Afriani Rina Dahlyanti Rinaldi Jasmi Rinita Amelia Risa Tiuria Risfaheri - Riska Padilah Riski Hernando Rita Rismala Rizaldy Arigi Rizky Andrian Rizqi Bayu Aji Robi Amizar Roby Dwi Hartanto Rohmat Gunawan Romy Adzani Adiputra Roseno, Rizky Haffiyan Rr. Amanda Pasca Rini, Rr. Amanda Pasca Ruh Devita Widhiana Prabowo Rusli, Ridho Kurniawan S. Syamsurizal Sa'diatul Fuadiyah Sahlya Handayati Salam N. Aritonang Sanusi Ibrahim Sarini Vita Dewi Sedyo Mukti, Putri Ayu Sepri Reski Setiyoko, Didik Tri Shaufiah . Sigit Budisantoso Silvia Atika Anggrayni Simon He Siti Rohani Sitorus, Angela Tiara Maharani Solly Aryza Sri Andayani Sri Melia Suci Aprianti Sukanta Sumaryati Syukur Suyitman Suyitman Syaifuddin Ahrom Syaifuddin Ahrom Syaiful Akmal Syamsul Hadi Tebay, Selvi Teguh Surya Apri Handoyo Theriana Ayu Waskitaning Tyas Thoriq Akhdan, Muh Titi Sumanti Tongku Nizwan Siregar Ufra Neshia Umar Ali Ahmad Urnemi - Urnemi Urnemi Utomo, Muhajir Veritia, Veritia Vici E.H.S. Susilowati Wibowo, Kurnia Drajat Winardhi, Sonny Winardhi, Sonny Wiratama, Arga Kusuma Wiwik Handayani Wizna Wizna Wulandari, Dinda Atikah Yani Riyani Yanuar Firdaus Yanuar Firdaus A Yanuar Firdaus A.W. Yesi Chwenta Sari Yoan Amri Akbar Yolani Utami Yudha E. Pratama Yudha Endra Pratama Yuherman Yuherman Yulia Murni Yulia Yellita Yuliant Sibaroni Yulianti Fitri Kurnia Yuliawati Yuliawati Yulisna Gita Hapsari Yundari, Yundari Yusabri Yusran Khery, Yusran Yusri Dianne Jurnalis Yusza Reditya Murti Zidni Mubarok Zoni Hidayat