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ZK Abdurahman Baizal
Universitas Telkom
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Healthy Food Recommender System for Obesity Using Ontology and Semantic Web Rule Language Aditya, Naufal; Baizal, Z. K. A.; Dharayani, Ramanti
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3005

Abstract

Today's lifestyle and eating patterns tend to be irregular due to busyness. People prefer eating foods that are fast and easy to obtain, but often lack knowledge of the nutritional content in them. These eating patterns lead to unbalanced nutrition and can cause various health problems and diseases, such as overweight and obesity. Due to a lack of information, people often turn to drugs instead of learning about healthy diets, making it difficult for them to determine what menu to choose or what type of food to consume. While there have been many studies to recommend healthy food based on user preferences, there is currently no recommender system that includes serving size and budget for each daily food recommendation that is implemented in a chatbot framework. This study proposes using ontology and the Semantic Web Rule Language (SWRL) to store knowledge in the ontology and then process it using SWRL to produce food recommendations based on user preferences. From a sample of user data which obtained 170 recommended meal menus. System performance is pretty good. Based on the validation results from nutritionists, the precision value was 0.852941, the recall was 1, and F-score of 0.920634 So that a healthy food recommendation system can be used to help the user follows a diet that meets his nutritional needs and is within his budget needed
Content-Based Music Recommender System Using Deep Neural Network Baizal, Z. K. A.; Andiety, Rich
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5762

Abstract

Music is one of the most popular forms of entertainment. Along with the development of information technology, music streaming platforms such as Spotify, Apple Music, and Deezer are increasingly popular among users. However, with thousands of songs available on these music streaming platforms, users often have difficulty finding songs that suit their tastes. Therefore, we design a music recommender system that can assist users in finding songs that are more in line with user preferences. In this research, we propose the development of a content-based music recommender system using a combination of Content-Based Filtering and Deep Neural Network (DNN) methods. The DNN used is Convolutional Neural Network (CNN) which serves to increase the percentage of accuracy to provide results that match user needs. This research aims to develop a music recommender system that can provide personalized recommendations to users according to the preferences of users. This research provides an accuracy result of 73.5%. From these results, it has been proven that the resulting music recommendations can be an alternative to the existing Collaborative Filtering-based recommender system.
Item-Based Collaborative Filtering Bandung Café Recommender System Using Recurrent Neural Network Baizal, Z. K. A.; Sitorus, Angela Tiara Maharani
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5772

Abstract

The aim of this study is to develop a dependable Cafe Recommender System for the Bandung area by employing a fusion of Item-Based Collaborative Filtering (IBCF) and Recurrent Neural Network (RNN) methodologies. The motivation behind this study stems from the growing need for more accurate and relevant café recommendations in Bandung, a city renowned for its diverse selection of cafes. Previous research has primarily focused on using either collaborative filtering or natural language processing approaches independently, leading to frequent limitations in understanding the entire context of user preferences and judgments. To address these shortcomings, we utilize the IBCF technique to analyze user rating data, identifying similarities amongst cafes to generate personalized recommendations. Concurrently, we employ the Recurrent Neural Network (RNN) method to examine and understand user reviews, facilitating a more advanced and contextually sensitive suggestion procedure. Our hypothesis posits that the amalgamation of IBCF (Item-Based Collaborative Filtering) and RNN (Recurrent Neural Network) will enhance the precision and pertinence of recommendations in the Bandung region. The assessment of the recommendations is conducted using measures such as Precision, Recall, and F1-score. The model demonstrates a precision of 89.04%, a recall of 88.75%, and an F1-score of 88.62%, which suggests that it is a suitable alternative to commonly used strategies for recommending cafes.
Ontology-Based Recommender System for Personalized Physical Exercise in Obesity Management Muhammad, Widi Sayyd Fadhil; Baizal, Z. K. A; Dharayani, Ramanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12689

Abstract

In Indonesia, obesity is a serious health issue, and rates have risen recently because of sedentary lifestyles and poor eating practices. We suggest a proactive self-care suggestion system specifically created for Indonesians who are dealing with obesity to address this problem. Our recommender system attempts to give customers individualized suggestions for healthy lifestyle modifications that will make it easier for them to manage their weight. Because social media is so widely used in Indonesia, we created our system as a Telegram Chatbot. Our system may provide personalized suggestions based on a particular gender, activity level, fat mass, and difficulty of exercise that are relevant to Indonesians by fusing the user's ontological profile with generic clinical guidelines and standards for the management of obesity. Ontologies with Semantic Web Rule Language (SWRL) were used in the development of our system since SWRL ontologies are thought to perform better. Evaluations carried out using case studies and expert verification illustrate the usefulness of our suggested method, and the validated result of 88.8 percent demonstrates that our system can deliver good suggestion results for the user.
Ontology-Based Food Menu Recommender System for Patients with Coronary Heart Disease Najla Nur Adila; Baizal, Z. K. A.
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12858

Abstract

Coronary heart disease is one of the leading causes of death. Knowledge of dietary patterns and proper food selection is an effort to address the risk and support coronary heart disease's healing process. Therefore, this study developed a food menu recommender system as a reference for patients with coronary heart disease. The recommender system is crucial in creating a proper dietary pattern for managing personalized meal plans. The system calculates the required nutritional needs of users. Ontology is used to represent knowledge about nutrition data and food intake. The ontology base with Semantic Web Rule Language (SWRL) enables the system to identify the most suitable foods for patients with coronary heart disease. We use SWRL rules to generate recommendation conclusions based on the existing ontology. Using this language enhances the descriptive logic capabilities, as the rules can overcome the limitations of the ontology language. Therefore, the system is built to find food menu options that match the required nutrition for patients. The nutritionist knowledge will be used to measure the system's performance compared to the recommendations made by nutritionists. From the user data sample, 150 recommended food menu data were obtained. The validation performance results obtained a precision 0.893, recall 1, and F_Score 94.3%.
Ontology-based Nutrition Recommender System for Stunting Patients Ramadhani, Nur Laili; Baizal, Z. K. A.
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12888

Abstract

Stunting is a growth disorder that occurs in early childhood. This condition occurs because the child has a chronic nutritional problem which triggers the child to have a height below normal. The indicator used as a standard for whether a child is stunted or not is height for age. If a child has a z-score value less than -2 standard deviations, then the child is said to suffer from stunting. Poor nutritional intake is one of the factors causing children to suffer from stunting. Most Indonesian people think that the genetics of both parents causes children to be shorter than their age, but genetics is a minimal factor that causes stunting. In 2020, Indonesia ranks second in the prevalence of stunting in Southeast Asia, according to the Asian Development Bank (ADB) report. Based on the results of the Indonesian Nutritional Status Survey (SSGI) in 2021, the stunting prevalence rate in Indonesia 2021 is 24.4%, but in 2022, the stunting prevalence rate will drop to 21.6%. One way to treat stunting in children is by providing daily nutritional intake according to the child's condition. In this study, we used the Telegram chatbot with an ontology and the rules Semantic Web Rule Language as a knowledge base. The accuracy performance of our system is 93.3% which shows that our system can provide nutritional recommendations for stunting patients.
Ontology-Based Food Recommender System for Nutrition in School-Age Wulandari, Dinda Atikah; Baizal, Z. K. A.
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12895

Abstract

Nutrition plays an important role in the body and child development. Therefore, it is very important for parent to understand the nutritional needs of children to grow healthy and smart. If nutritional intake is not met, malnutrition can occur in children it interferes whit their growth and development process. The food recommendation system in this study is based on knowledge modeling. The focus of the research is to develop a recommendation system using ontology with Semantic Web Rule Language (SWRL) and form a knowledge base according to the guidelines proposed by Recommended Nutrient Intakes (RNI). Additionally, an Artificial Intelligence (AI) telegram chatbot named NutritionChildreBot was developed for this purpose. The recommended food menu is following the nutritional needs of children aged 7-9 years. The acquired knowledge base will be managed to provide information to users. The results of this research evaluation are in the form of recommendations for selecting foods that meet children’s nutritional needs based on information obtained from reliable sources.Based on this value, the calculation of precision, memory, and F_Score obtained is 97,9% of the accuracy of the results recommended by the system
Laptop Recommender System Using the Hybrid of Ontology-Based and Collaborative Filtering Putra, A. D. A.; Baizal, Z. K. A.
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.13370

Abstract

In the era of ever-evolving information technology, choosing the best laptop can be a complicated task for many users. The increasing complexity of technical specifications is often an obstacle, especially for users who need help understanding them. In addressing this challenge, we propose a solution: a laptop recommendation system that considers users' preferences and functional needs. We designed this system to help users choose a laptop that suits their daily functional needs. This system uses a form of Conversational Recommender System (CRS) by combining Ontology-Based Recommender System Filtering and Collaborative Filtering (CF). Ontology-Based Recommender System Filtering ensures a strong relationship between functional needs and technical specifications of laptops, making it easier for users to identify the right laptop. At the same time, Collaborative Filtering (CF) can provide diversity to the recommended products by using similar user preference data. We evaluate the accuracy of our system by calculating the success rate of recommendation accuracy with the accuracy metric, and the evaluation results show that the success rate of recommendation accuracy reaches 93.33%. Our system is highly effective in assisting users in choosing a laptop that suits their functional needs. With our laptop recommendation system, users can confidently select the correct laptop without being burdened by technical specifications, thus making their lives easier and more productive.
Physical Activities Recommender System Based on Sequential Data Use K-Mean Clustering Roseno, Rizky Haffiyan; Baizal, Z. K. A.; Dharayani, Ramanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13374

Abstract

Physical activities such as Exercise are essential in maintaining health and fitness, especially for those who adopt a healthy lifestyle. Irregularity in doing Exercise can hurt the body and health, especially if it is not done according to one's physical capacity. In the framework of this research, we developed a Recommender System that aims to provide exercise suggestions according to the user's preferences, especially in the categories of cycling, running, walking, and horse riding. The primary considerations of the variables include heart rate (Average Heart Rate) and pace (Speed Rate). This research approach uses the FitRec Dataset and applies the K-Mean Clustering Algorithm, with the support of APACHE SPARK, for large-scale data processing, given the large data size in the FitRec dataset. Grouping is done using the FitRec dataset and K-Mean. Users are grouped according to heart rate and pace information; this provides appropriate Exercise for users. The test results show that the proposed system performs well, as indicated by the silhouette score = 0.596, calinzski-harabaz score = 2133.09, and davies bouldin score = 0.480. These test metrics reflect the system's ability to cluster. Indirectly, the accuracy performance of the system is assessed through these metrics, showing good accuracy test results.
Ontology-based Food Menu Recommender System for Pregnant Women Using SWRL Rules Fadillah, Ichsan Alam; Baizal, Z. K. A.
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13722

Abstract

Pregnancy is a crucial period in a woman's life because her body must prepare and support the growth and development of the fetus. During pregnancy nutritional needs will increase. Lack of nutritional intake during pregnancy can cause serious health problems, one of which is anemia. However, excess nutrition during pregnancy also has a negative impact on pregnant women. Therefore, a recommender system is required to provide food menu recommendations according to the daily nutritional needs of pregnant women. Currently, there has been a lot of research on ontology-based food recommender systems that can provide food recommendations to users, but there is no research that specifically provides food menu recommendations that suit the needs of pregnant women. Therefore, in this research, we propose an ontology-based food menu recommender system using SWRL (Semantic Web Rule Language) rules for pregnant women. In this food menu recommender system, ontology is used to represent food knowledge and its nutritional content, and SWRL rules are used to reason logical rules in the ontology to determine the appropriate food menu for pregnant women. This recommender system also considers diseases and allergies that pregnant women have so that it can provide food menu recommendations that are more suitable for users. From 15 data samples from pregnant women, the system provides 75 food menu recommendations for pregnant women. Based on the validation results that have been carried out, the precision value is 0.986, the recall is 1, and the F1-score is 0.992.
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 Aryanta, Rafi Rizkya Ayunda Farah Istiqamah 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 Dhiva Rezzy Pratama Diah Mahmuda Diah Pudi Langgeni Didik Tri Setiyoko 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 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 Farma 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 Muhammad, Widi Sayyd Fadhil Mustakim, ' Musthafa Zaki Bahar Mustofa, Mutmainnah 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 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 Shaufiah . Sigit Budisantoso Silvia Atika Anggrayni Simon He Siti Rohani Sitorus, Angela Tiara Maharani Sri Andayani Sri Melia Suci Aprianti Sukanta Sumaryati Syukur Suyitman Suyitman Syaifuddin Ahrom Syaifuddin Ahrom 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 Yusran Khery, Yusran Yusri Dianne Jurnalis Yusza Reditya Murti Zidni Mubarok Zoni Hidayat