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All Journal Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA J-SAKTI (Jurnal Sains Komputer dan Informatika) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) eProceedings of Engineering Tekmulogi: Jurnal Pengabdian Masyarakat
Ramanti Dharayani
Telkom University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Conversational Recommender System for Impromptu Tourists to Recommend Tourist Routes Using Haversine Formula Liviandra, Monica; Baizal, Z K Abdurahman; Dharayani, Ramanti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3229

Abstract

In this paper, we use two terms to describe tourists, i.e. planned tourists and impromptu tourists. Planned tourists are tourists who intentionally travel. Meanwhile, impromptu tourists are those who accidentally become tourists because they are in a new area for an activity. Previously, tourists who were going to travel usually relied on the services of travel agents to get recommendations for tourist attractions, different from impromptu tourists this was not done before. Impromptu tourists sometimes do not have much time to carry out tourism activities so that impromptu tourists only visit the closest tourist attractions from their location. Lack of experience in a new area and only relying on information on the internet makes it difficult for tourists to find tourist attractions based on their preferences. One solution to this problem is that a system is needed that can recommend tourist attractions in terms of distance by considering tourist preferences. In this study, we developed a conversational recommender system (CRS) to obtain user preferences. For the method we use the Haversine Formula to calculate the distance. The results of this study are a web application that recommends tourist attractions and routes to several tourist attractions, which can be done at one time. Based on the evaluation of the time complexity in the route search, linear complexity is obtained which shows good performance with optimal conditions.
Multi Criteria Recommender System for Music using K-Nearest Neighbors and Weighted Product Method Muhamad Hafidh Nofal; zk abdurahman baizal; Ramanti Dharayani
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.575

Abstract

Currently, the music industry has grown rapidly which has led to an information overload that hinders users from finding the music they want, because everyone has their own unique characteristics. In a previous study, the Recommender System converted music lyrics into digital values using Lexicon's Non-Commercial Research (NRC) and K Nearest Neighbors (KNN) to look for similarities between music. However, this system only uses lyrics to recommend music, so it doesn't pay more attention to user preferences. Therefore, in this study adds criteria from users using the Weighted Product Method (WPM) to weight the music criteria with the input criteria from users. In this study uses a music dataset from 2000 to 2019 taken from the Kaggle website. The purpose of this study was to measure user satisfaction using the System Usability Scale (SUS). In this case, the user is free to answer 10 questions regarding the results of the recommendations provided by the system. Based on the results of the questionnaire, the SUS score was 83.65. This score is included in the EXCELLENT category with grade A scale
Tourist Places Recommender System Using Cosine Similarity and Singular Value Decomposition Methods Theriana Ayu Waskitaning Tyas; Z K Abdurahman Baizal; Ramanti Dharayani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3151

Abstract

Tourism in the city of Bandung has various potentials in the field of culture, regional specialties, buildings, and other tourist attractions. On the Tripadvisor page there are many reviews from users who have visited tourist attractions in the city of Bandung. In this case, user reviews are an important element for analysis. The analysis process is carried out using rule-based sentiment analysis. In conducting the review analysis, we use vaderSentiment to weight the positive and negative values. Positive values are subtracted from negative values to get a compound value and converted to a rating value. The rating value obtained is then processed using the Cosine Similarity and Singular Value Decomposition methods to obtain recommendations for tourist attractions in the city of Bandung. For this method, we use the Root Mean Square Error method as a measure of the level of accuracy between the predicted values. The results of the measurement of the level of accuracy produce a value of 3,489 in the Cosine Similarity method, while the Singular Value Decomposition method gets a value of 1,231. The value in the Singular Value Decomposition method is smaller than the Cosine Similarity method with a difference of 2,258 values
Question Answering System at the Kingdom of Sumedang Larang with Naïve Bayes Method Richo Fedhia Saldhi; Z.K.A. Baizal; Ramanti Dharayani
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2079

Abstract

The Sumedang Larang Kingdom is one of the kingdoms in Indonesia which was founded by Prabu Tajimalela in 721 AD. The Sumedang Larang Kingdom is known as the national history of Indonesia. Still, most of the current generation does not know the history of the Sumedang Larang Kingdom, especially the younger generation. Therefore, we developed a question-and-answer system to seek information about the Sumedang Larang Kingdom. With the development of information technology, research on question answering systems is applied to research on Biomedical Questions to produce correct answers. Our system will help literacy about the Sumedang Larang Kingdom for the younger generation, especially students, and increase Indonesian cultural assets. The QA system aims to generate and provide precise short answers to user questions by automatically using information extraction and natural language processing methods. To collect and create questions, we use the concept of ontology. In addition, we use the Natural Language Naïve Bayes method to answer user questions. We built a QA system that can help students find information about the history of the Sumedang Larang Kingdom. Based on the accuracy of the results of testing the method we propose. In our evaluation, we involve the Decision Tree method as the base model. We note that the accuracy of the Naïve Bayes method is higher than that of the Decision Tree. The accuracy result of Naïve Bayes at the ratio of 8:2 and 7:3 is 67%, while the Decision Tree is only 56%.
QUESTION ANSWERING FOR SUMEDANG LARANG KINGDOM USING THE MULTILAYER PERCEPTRON ALGORITHM Arifa Nur Hasanah; Abdurahman Baizal; Ramanti Dharayani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 7, No 4 (2022)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v7i4.3206

Abstract

In Indonesia, there were many kingdoms in the past, one of which was the Sumedang Larang Kingdom. Sumedang Larang is an Islamic kingdom under the control of the Pajajaran Kingdom. Through history, a nation will be able to recognize the origin of its own nation. Therefore, teaching about history is very important to be instilled from an early age. Through the rapid development of technology, teaching today is not only in the form of formal teaching, but also informal teaching. Nowadays, informal teaching can be done through various media, one of the media that is often used is gadgets. The utilization of gadgets as learning media allows a person to learn independently. One form of history learning by utilizing gadgets can be in the form of a Question Answering System (QAS). QAS allows users to ask questions using natural language and the system will answer the questions. Therefore, our research aims to help introduce the history of the Sumedang Larang kingdom to the public. We build a QAS by utilizing the n-gram model, Multilayer Perceptron (MLP) algorithm, and ontology. N-gram is used to cut words/sentences and convert them into a matrix, while MLP is used to classify texts, and ontology is used as knowledge representation. In this study, the system was able to answer 35 questions out of 61 questions, so it had an accuracy of 57.37%.
Healthy Menu Recommendation for Malnutrition Patients Based on Ontology Igga Febrian Virgiani; Z K A Baizal; Ramanti Dharayani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5543

Abstract

A healthy diet is one of the keys to creating a healthy lifestyle, but at this time the selection of a healthy and nutritious meal menu in the society is difficult to do because of the limited nutritional information contained in a food. A healthy diet can help a person to get balanced nutrition, good nutritional intake can increase the body's immunity, and make a normal or healthy body weight so that it can increase work productivity and prevention of chronic diseases. To overcome this problem, we propose the use of ontology and Semantic Web Rule Language (SWRL) to build a healthy menu recommendation system in the form of a chatbot to make it easier for users to determine the daily meal menu. These recommendations are personalized by considering the user's needs. Ontology is used to represent the required knowledge and the reasoning process uses SWRL. From the results of system testing, the recommendations get the accuracy of the F-Score value of 0.951
Implementasi ETL (Extract, Transform, Load) Pangkalan Data Perguruan Tinggi dengan Menggunakan State-Space Problem Ramanti Dharayani; Kusuma Ayu Laksitowening; Amarilis Putri Yanuarifiani
eProceedings of Engineering Vol 2, No 1 (2015): April, 2015
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Extraction, Transformation, dan Load (ETL) adalah salah satu proses pada datawarehouse. Proses dari ETL adalah mengumpulkan data dari berbagai macam sumber. ETL adalah proses untuk mengolah data menjadi data yang bersih sesuai dengan ketentuan datawarehouse. Proses ETL pada umumnya terdiri dari berbagai macam aktivitas dan membutuhkan waktu serta memori yang cukup besar. Pada tugas akhir ini akan dilakukan implementasi ETL dengan menggunakan alur kerja state space problem pada kasus Pangkalan data perguruan tinggi. State space problem digunakan untuk menggambarkan alur proses ETL dan mencari urutan aktivitas pada proses ETL. Dari hasil pengujian ETL dilakukan perubahan urutan aktivitas dengan menggunakan transisi graf dan didapatkan hasil yang lebih optimal. Keywords— Extract, Transform, Load, ETL, state space problem, data warehouse, oracle warehouse builder
Pelatihan media e-learning classroom untuk guru SMKN 1 Peureulak Timur Ichwanul Muslim Karo Karo; Widi Astuti; Ramanti Dharayani
TEKMULOGI: Jurnal Pengabdian Masyarakat Vol 2, No 2 (2022): November 2022
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3755.966 KB) | DOI: 10.17509/tmg.v2i2.48963

Abstract

One of the sectors affected by the covid-19 pandemic is the education sector. SMKN 1 Peureulak Timur as the only vocational high school in Peureulak Timur sub-district, East Aceh must continue to provide education services for all students of the best quality. One of the efforts to improve educating and teaching skills for teachers at SMKN 1 Peureulak Timur is by providing e-learning training. One of the e-learning media that is often used is Goggle Classroom because of its complete features and quite easy to operate. Therefore, the activity that will be carried out at this community dedication is Google Classroom training. It is hoped that after receiving this Google Classroom training, teachers of SMKN 1 Peureulak Timur can improve their performance in facing the Industrial Revolution 4.0.
Ontology-Based Physical Exercise Recommender System for Underweight Using Ontology and Semantic Web Rule Language Christhofer Laurent Juliant; Z. K. A. Baizal; Ramanti Dharayani
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3823

Abstract

Inactive lifestyles and unhealthy diets are often the result of people's busy lives. because of these bad habits, many people are underweight. diet and lack of physical activity are factors that cause underweight. Due to lack of information, people prefer to live lazily and not exercise. To solve this problem, we propose a physical exercise recommendation system that is explicitly designed for Indonesian people who are struggling with underweight. Despite the existence of various research studies advocating for physical activities tailored to individual preferences, there is currently no recommendation system available within a chatbot framework that includes a comprehensive session to be completed, along with specific sets and repetitions for each activity. This research proposes the utilization of ontology and Semantic Rule Web Language (SWRL) to represent and process the knowledge presented, enabling the development of rules for generating physical activity recommendations based on user preferences. By integrating the user profile, ontology, and the rules created, our system recommends physical exercise based on gender, weight, height, activity level, difficulty of movements, and the type of muscle to be trained. From the sample user data obtained, 408 physical exercises menu are recommended. The performance of the system is quite good, together with the validation results from personal trainers, obtained a precision value of 0.8, recall of 1, and f-score of 0.888. Concluding that the system we designed can provide physical activity recommendations in accordance with user preferences.
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
Co-Authors Ade Romadhony Aditya, Naufal Amarilis Putri Yanuarifiani Arifa Nur Hasanah Baizal, Z. K. A Barawi, Mohamad Hardyman Christhofer Laurent Juliant Donni Richasdy Hasmawati, H Ichwanul Muslim Karo Karo Igga Febrian Virgiani Imelda Atastina Kemas Rahmat Saleh Wiharja Laksitowening, Kusuma Ayu Liviandra, Monica Muhamad Hafidh Nofal Muhammad, Widi Sayyd Fadhil Nofal, Muhamad Hafidh othman, mohd kamal Pratama, Reyhan Richasdy , Donni Richo Fedhia Saldhi Ridho Akbar Roseno, Rizky Haffiyan RR. Ella Evrita Hestiandari Septariken, Mohammad Fajra Theriana Ayu Waskitaning Tyas Widi Astuti Yanuarfiani, Amarilis Putri ZK Abdurahman Baizal