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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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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.
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.
Covid-19 Prediction Using Lightgbm and LSTM Dharayani, Ramanti; Hasmawati, H; Khotijah, Siti
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.495

Abstract

Covid-19 has become a global health problem during this pandemic. Every country is struggling to fight this problem as well as Indonesia. Indonesia has a high number of new cases and this has an impact on the high demand for bed occupancy rates. To overcome this situation, we recommend the prediction of covid-19 using LGBM and LSTM. We implement two pre-processing, namely one-hot data encode and Normalization. The results of the pre-processing will be used as input for the prediction of new cases of COVID-19 using the LGBM and LSTM algorithms. The experimental results show that LSTM has better results than LGBM. We evaluated that the number of epochs we used in the LSTM had a large influence on the RMSE, MAE, and R2 measurements
Covid-19 Prediction Using Lightgbm and LSTM Dharayani, Ramanti; Hasmawati, H; Khotijah, Siti
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.495

Abstract

Covid-19 has become a global health problem during this pandemic. Every country is struggling to fight this problem as well as Indonesia. Indonesia has a high number of new cases and this has an impact on the high demand for bed occupancy rates. To overcome this situation, we recommend the prediction of covid-19 using LGBM and LSTM. We implement two pre-processing, namely one-hot data encode and Normalization. The results of the pre-processing will be used as input for the prediction of new cases of COVID-19 using the LGBM and LSTM algorithms. The experimental results show that LSTM has better results than LGBM. We evaluated that the number of epochs we used in the LSTM had a large influence on the RMSE, MAE, and R2 measurements
IMPLEMENTATION OF EXTRACT, TRANSFORM, LOAD (ETL) ON UNIVERSITY DATABASE USING STATE-SPACE PROBLEM Dharayani, Ramanti; Laksitowening, Kusuma Ayu; Yanuarfiani, Amarilis Putri
TEKTRIKA Vol 9 No 1 (2024): TEKTRIKA Vol.9 No.1 2024
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v9i1.6952

Abstract

Extraction, Transformation, and Load (ETL) adalah salah satu proses dalam data warehouse yang mengumpulkan data dari berbagai sumber. ETL mengolah data mentah menjadi data yang bersih sesuai denganketentu-an data warehouse. Proses tersebut umumnya terdiri atas beberapa aktivitas dan membutuhkan waktuyang lama dan memori yang besar. Penelitian ini melakukan implementasi ETL dengan menggunakan workflowmasalah state-space pada kasus database universitas. Masalah state-space menggambarkan aliran proses ETL danmenemukan urutan aktivitas dalam proses ETL. Dari hasil uji ETL, rangkaian kegiatan diubah menggunakantransisi graf dan diperoleh hasil yang lebih optimal.
Multi Criteria Recommender System for Music using K-Nearest Neighbors and Weighted Product Method Nofal, Muhamad Hafidh; baizal, zk abdurahman; Dharayani, Ramanti
Indonesian 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
BOARDING HOUSE RECOMMENDATION WITH COLLABORATIVE FILTERING USING THE GENERATIVE ADVERSARIAL NETWORKS (GANS) METHOD Septariken, Mohammad Fajra; Richasdy , Donni; Dharayani, Ramanti
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1598

Abstract

This research represents a concerted effort to tackle the pressing challenge of facilitating a personalized and efficient boarding house recommendation system tailored to individual user preferences, particularly among students. The overarching objective is to streamline and simplify the often arduous task of locating suitable accommodations by harnessing the potential of Collaborative Filtering. The deliberate selection of Collaborative Filtering as the cornerstone of this recommendation system stems from its proven efficacy in scrutinizing intricate user behavior patterns and deriving precise, tailored recommendations. Leveraging historical boarding house data, this methodology meticulously identifies patterns and similarities among users to offer suggestions finely aligned with their specific preferences. Integral to this research methodology is the concurrent utilization of Generative Adversarial Networks (GANs), serving a pivotal role in evaluating the system's accuracy. This dual-pronged approach, amalgamating Collaborative Filtering for recommendation generation and GANs for accuracy assessment, aims to ensure the system's efficacy in delivering precise, individualized suggestions. The findings of this study underscore a promising outcome – a system proficient in furnishing boarding house recommendations remarkably attuned to user preferences. This system's potential transcends the realm of student housing, presenting opportunities for broader applications across diverse fields requiring personalized recommendation systems. Crucially, the study's meticulous optimization of the GANs model, involving meticulous parameter adjustments including epoch count, optimizer selection (Adam), employment of mean absolute error (MAE) function, and fine-tuning a learning rate of 0.002, culminated in an outstanding achievement. The resultant MAE value of 0.0180 denotes minimal prediction errors, signifying estimations remarkably proximate to actual test data values, thus solidifying the system's reliability and precision. Ultimately, the successful development and evaluation of this boarding house recommendation system hold profound implications, promising to significantly enhance student experiences in discovering accommodations aligned with their preferences. Furthermore, this study's methodological approach paves the way for future research and wider applications in diverse domains seeking effective, personalized recommendation systems.
SAFE NUSANTARA: A semi-automatic framework for engineering and populating a Nusantara Food Ontology Wiharja, Kemas Rahmat Saleh; Barawi, Mohamad Hardyman; Romadhony, Ade; Atastina, Imelda; Dharayani, Ramanti; Othman, Mohd Kamal
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1042

Abstract

Constructing a comprehensive food ontology, particularly for culturally diverse cuisines like Southeast East Asian (Nusantara), is hindered by the variability of online recipes and the scarcity of structured data. This research introduces SAFE Nusantara, a novel semi-automated system designed to build and populate a Nusantara food ontology by extracting relevant terms from diverse online sources in Indonesian and Malaysian languages. By leveraging a combination of techniques, including topic modelling, natural language processing, and knowledge graph techniques, SAFE Nusantara addresses the challenges of data format diversity and language specificity. The system has demonstrated significant improvements in the accuracy of food classification and has the potential to enhance food recommendation systems and cultural heritage preservation efforts.
Sistem Rekomendasi Buku Dengan Collaborative Filtering Menggunakan Metode Singular Value Decomposition (SVD) Akbar, Ridho; Richasdy, Donni; Dharayani, Ramanti
eProceedings of Engineering Vol. 10 No. 5 (2023): Oktober 2023
Publisher : eProceedings of Engineering

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

Abstract

Abstrak— Buku/novel adalah salah satu media hiburan yang tidak pernah luput oleh zaman. Bagi penikmatnya, buku adalah suatu hal yang sangat penting karena buku merupakan suatu hiburan yang akan dibaca sesuai dengan suasana hati mereka. membaca juga merupakan jendela dunia, Dikarenakan banyaknya judul – judul buku yang telah rilis,hal tersebut membuat banyak orang sulit dalam memilih buku yang ingin mereka baca. Dalam permasalahan ini dibutuhkan suatu sistem yang dapat memudahkan pengguna dalam mencari buku atau novel yang sesuai dengan minat mereka, sebuah sistem rekomendasi dirasa mampu untuk memecahkan permasalahan ini. Maka dari itu penilitian ini membangun sebuah sistem rekomendasi buku dengan Userbased Collaborative Filtering menggunakan metode singular value decompsotion (SVD). Dan dilakukan pengukuran akurasi menggunakan metode MAE dan MSE dan didapatkan hasil akurasi MAE sebesar 0,7063 dan MSE sebesar 0,913.Kata kunci — Sistem Rekomendasi, User Based Collaborative Filtering, SVD.
Sistem Rekomendasi Collaborative Filtering Pada Smartphone Menggunakan K-Means Pratama, Reyhan; Richasdy, Donni; Dharayani, Ramanti
eProceedings of Engineering Vol. 10 No. 5 (2023): Oktober 2023
Publisher : eProceedings of Engineering

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

Abstract

Abstrak — Smartphone memenuhi kebutuhan user dengan menyediakan berbagai layanan komunikasi yang memungkinkan seperti transfer informasi dalam bentuk teks, grafik, suara, dan layanan Internet. Banyak dari masyarakat kebingungan untuk memilih dari banyak nya merk dan tipe yang beredar di pasar saat ini. Maka dari itu penelitian ini melakukan pemberian rekomendasi dengan perbandingan prediksi rating smartphone menggunakan metode K-Means dengan membandingkan tiga perhitungan similarity diantaranya Pearson, Pearson Baseline dan Cosine, dan penggunaan jumlah tetangga yang bervariatif. Dilakukan perbandingan tingkat kinerja antara skenario yang berbeda. Berdasarkan perhitungan dan analisis yang sudah dilakukan, didapatkan skenario antara penggunaan jumlah trainset 80% dan testset 20%, metode similarity Pearson Baseline, dan 90 jumlah tetangga menghasilkan nilai error terkecil dengan nilai RMSE 0.6599 yang merupakan skenario K-Means dengan kinerja paling tinggi dalam penelitian ini. Sedangkan skenario penggunaan jumlah trainset 70% dan testset 30%, metode similarity Pearson, dan 10 jumlah tetangga menghasilkan nilai error terbesar dengan nilai RMSE 0.7279 yang berarti skenario tersebut memiliki kinerja paling rendah.Kata Kunci— Smartphone, K-Means, User-based Collaborative Filtering, Similarity, RMSE.
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