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All Journal Sistemasi: Jurnal Sistem Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah
Fandy Setyo Utomo
Universitas AMIKOM Purwokerto
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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MUSIC RECOMMENDATION SYSTEM BASED ON COSINE SIMILARITY AND SUPERVISED GENRE CLASSIFICATION Jamie Mayliana Alyza; Fandy Setyo Utomo; Yuli Purwati; Bagus Adhi Kusuma; Mohd Sanusi Azmi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 9 No 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4324

Abstract

Categorizing musical styles can be useful in solving various practical problems, such as establishing musical relationships between songs, similar songs, and finding communities that share an interest in a particular genre. Our goal in this research is to determine the most effective machine learning technique to accurately predict song genres using the K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) algorithms. In addition, this article offers a contrastive examination of the K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) when dimensioning is considered and without using Principal Component Analysis (PCA) for dimension reduction. MFCC is used to collect data from datasets. In addition, each track uses the MFCC feature. The results reveal that the K-Nearest Neighbors and Support Vector Machine offer more precise results without reducing dimensions than PCA results. The accuracy of using the PCA method is 58% and has the potential to decrease. In this music genre classification, K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) are proven to be more efficient classifiers. K-Nearest Neighbors accuracy is 64,9%, and Support Vector Machine (SVM) accuracy is 77%. Not only that, but we also created a recommender system using cosine similarity to provide recommendations for songs that have relatively the same genre. From one sample of the songs tested, five songs were obtained that had the same genre with an average accuracy of 80%.
Rectified Linear Units and Adaptive Moment Estimation Optimizer on ANN with Saved Model Prediction to Improve The Stock Price Prediction Framework Performance Sekhudin Sekhudin; Yuli Purwati; Fandy Setyo Utomo; Mohd Sanusi Azmi; Pungkas Subarkah
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1586.271-282

Abstract

A stock is a high-risk, high-return investment product. Prediction is one way to minimize risk by estimating future prices based on past data. There are limitations to solving the stock prediction problem from previous research: limited stock data, practical aspects of application, and less than optimal stock price prediction results. The main objective of this study is to improve the prediction performance by formulating and developing the stock price prediction framework. Furthermore, the research provides a stock price prediction framework that can produce better prediction results than the previous study with fast computation time. The proposed framework deals with data generation, pre-processing and model prediction. In further, the proposed framework includes two prediction methods for predicting stock closing prices: stored model prediction and current model prediction. This study uses an artificial neural network with Rectified Linear Units as an activation function and Adam Optimizer to predict stock prices. The model we have built for each forecasting method shows a better MAPE value than the model in previous studies. Previous research showed that the lowest MAPE was 1.38% for TLKM shares and 0.81% for BBRI. Our proposed framework based on the stored model prediction method shows a MAPE value of 0.67% for TLKM shares and 0.42% for BBRI. While the current model prediction method shows a MAPE value of 0.69% for TLKM shares and 0.89% for BBRI. Furthermore, the stored model prediction method takes 1.0 seconds to process a single prediction request, while the current model prediction takes 220 seconds.
PROGRAM PENDAMPINGAN PENULISAN ILMIAH UNTUK MENINGKATKAN JUMLAH PUBLIKASI ILMIAH MAHASISWA Sarmini Sarmini; Rujianto Eko Saputro; Fandy Setyo Utomo; Hanif Hidayatulloh; Ria Indriyani; Rio Fadly Ramadhan
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 7, No 4 (2023): Desember
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v7i4.17570

Abstract

ABSTRAKKurangnya pengetahuan dan keterampilan mahasiswa Fakultas Ilmu Komputer untuk menulis artikel ilmiah menyebabkan rendahnya jumlah luaran jurnal ilmiah yang dihasilkan oleh mahasiswa sebagai luaran program Merdeka Belajar Kampus Merdeka (MBKM) di semester gasal tahun akademik 2022/2023. Berdasarkan permasalahan tersebut, kami mengusulkan pelaksanaan kegiatan webinar pendampingan penulisan jurnal ilmiah bagi mahasiswa Fakultas Ilmu Komputer yang bertujuan untuk meningkatkan kemampuan menulis artikel ilmiah dan meningkatkan jumlah publikasi ilmiah mahasiswa sebagai luaran program Merdeka Belajar Kampus Merdeka. Ada tiga tahapan dalam pelaksanaan kegiatan yaitu tahap persiapan, implementasi dan evaluasi. Kegiatan pendampingan dilakukan secara daring dengan menghadirkan narasumber dari Universiti Teknikal Malaysia (UTeM) dan diikuti oleh 117 mahasiswa yang mengikuti program MBKM di semester genap 2022/2023. Dampak dari kegiatan webinar yang telah dilaksanakan yakni meningkatnya jumlah publikasi artikel ilmiah mahasiswa Fakultas Ilmu Komputer sebagai luaran program MBKM di semester genap tahun akademik 2022/2023. Berdasarkan data yang diperoleh dari Fakultas Ilmu Komputer Universitas AMIKOM Purwokerto terdapat sejumlah 192 tulisan artikel ilmiah mahasiswa yang telah dikirimkan ke beberapa jurnal nasional. Kata kunci: assistance; merdeka belajar kampus merdeka; scientific writing; collaboration; research ABSTRACTComputer Science Faculty students' lack of knowledge and skills to write scientific articles has resulted in the low number of scientific journals produced by students as outputs of the Merdeka Belajar Kampus Merdeka (MBKM) Program in the odd semester of the 2022/2023 academic year. Based on these problems, we propose implementing a webinar to assist scientific journal writing for students of the Faculty of Computer Science, which aims to improve the ability to write scientific articles and increase the number of students' scientific publications as an output of the Merdeka Belajar Kampus Merdeka program. There are three stages in implementing activities: preparation, implementation, and evaluation. Mentoring activities were carried out online by presenting resource persons from Universiti Teknikal Malaysia (UTeM) and were attended by 117 students taking part in the MBKM program in the even semester 2022/2023. The impact of the webinar activities that have been carried out is the increase in the number of publications of scientific articles by Faculty of Computer Science students as an output of the MBKM program in the even semester of the 2022/2023 academic year. Based on data from the Faculty of Computer Science, Universitas AMIKOM Purwokerto, 192 student scientific articles have been submitted to several national journals. Keywords: accompaniment; study center; games; collaboration; research
ALGORITMA NAÏVE BAYES UNTUK ANALISIS SENTIMENT REVIEW BLIBLI.COM DI GOOGLE PLAY STORE Siti Nur Fadhilah; Fandy Setyo Utomo
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i2.3887

Abstract

Currently, there are approximately 354 million active mobile phones in Indonesia, placing the country fourth globally in terms of the highest number of mobile phone users. E-Commerce, as a form of online transaction, enables the digital exchange of goods and services to meet daily needs. This research aims to implement sentiment analysis using the Naive Bayes classification algorithm as a method to gather user opinions. Thus, the study not only provides insights into customer satisfaction with Blibli.com but also serves as a basis for potential improvements in services or feature development to enhance the online shopping experience. Overall, the Naive Bayes algorithm successfully achieved an accuracy of around 84%, demonstrating its proficiency in categorizing sentiment in reviews. When focusing on negative data, the Naive Bayes algorithm exhibited a precision of approximately 79%, recall of around 95%, and an f1-score of about 86%, indicating its success in identifying and classifying negative reviews with high precision and sensitivity. On the positive side, the Naive Bayes algorithm achieved a precision of about 91%, recall of around 83%, and an f1-score of about 87%.
Sentiment Analysis of pegipegi.com Review on Google Play Store with Naïve Bayes Muhamad Naufal Burhanuddin Balit; Fandy Setyo Utomo
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.3913

Abstract

In the current era, a shift in consumer behavior is evident in the use of online platforms for booking tickets, involving various services such as flights, hotels, trains, buses, and entertainment. PegiPegi.com, as a rapidly growing online travel agent in Indonesia, demonstrates success by understanding the value of technology and maintaining strong partnerships. This phenomenon also impacts sentiment analysis, where users of this platform often provide reviews. This research aims to apply the Naïve Bayes classification method in sentiment analysis of PegiPegi.com reviews, focusing on understanding customer satisfaction and service improvement. By combining these approaches, the study contributes to a deeper understanding of user responses to OTA services and presents the evaluation results of the Multinomial Naive Bayes classification model with an accuracy rate of 89.5%. The high precision in the Negative class indicates the model's ability to identify negative reviews. However, there are challenges in classifying the Neutral class, suggesting potential for further improvement. Nevertheless, the F1-score of 0.522 reflects a good balance between overall precision and recall.
Co-Authors Bagus Adhi Kusuma Hanif Hidayatulloh Jamie Mayliana Alyza Mohd Sanusi Azmi Muhamad Naufal Burhanuddin Balit Pungkas Subarkah Ria Indriyani Rio Fadly Ramadhan Rujianto Eko Saputro Sarmini - Sarmini Sekhudin Sekhudin Siti Nur Fadhilah Yuli Purwati