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All Journal AMIKOM ICT AWARD 2010 TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Pro Bisnis Telematika Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Sains dan Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah RESEARCH : Computer, Information System & Technology Management JURTEKSI Jurnal Teknologi Terpadu Jurnal Pengabdian Kepada Masyarakat (Mediteg) Indonesian Journal of Electrical Engineering and Computer Science Madani : Indonesian Journal of Civil Society Jurnal Algoritma Jurnal Pengabdian Mitra Masyarakat (JPMM)
Yuli Purwati
Universitas Amikom Purwokerto
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : JITK (Jurnal Ilmu Pengetahuan dan Komputer)

 1 
DECISION SUPPORT AND MONITORING SYSTEMS FOR HUMANITARIAN PROGRAMS USING FORWARD CHAINING METHOD Purwati, Yuli; Pambudi, Aldi Setia
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 7 No 2 (2022): JITK Issue February 2022
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1226.006 KB) | DOI: 10.33480/jitk.v7i2.2813

Abstract

One of the covid-19 pandemic impacts is the emergence of new social problems. Humanitarian agencies are helping the government's role to overcome various social issues that arise through its humanitarian programs. One of which is with the MSR program from Aksi Cepat Tanggap Purwokerto. With increasing social problems and the complexity of improving data collection due to the covid-19 pandemic, the methods' limitations previously using paper will be difficult, and the limits of people who have the expertise to make decisions related to the submission of prospective beneficiaries. A decision support system is needed to provide suggestions of results under the rules that have will determining and monitoring and filing systems to provide convenience in managing data collection.
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%.
AN INNOVATIVE LEARNING ENVIRONMENT: G-MOOC 4D TO ENHANCE VISUAL IMPAIRMENTS LEARNING MOTIVATION Rujianto Eko Saputro; Berlilana Berlilana; Wiga Maulana Baihaqi; Sarmini Sarmini; Yuli Purwati; Fandy Setyo Utomo
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5037

Abstract

The proliferation of visual impairment among school-age children in Indonesia has prompted the need for specialized online learning solutions. The G-MOOC 4D platform, a novel Learning Management System (LMS), is designed to address this need by leveraging gamification and artificial intelligence to enhance accessibility for visually impaired users. This study reports on the development and testing of two AI models within the G-MOOC 4D framework: a facial recognition model for secure user authentication and a voice command model for interactive learning. User Acceptance Testing (UAT), conducted with expert users, namely teachers at a special needs school, showed high approval rates for the platform's features. The results show that all metrics, accuracy, precision, and recall reach their optimal values at a distance of 40 cm for face detection. The respective metric scores at that distance, precision: 100%, accuracy: 98%, and recall: 97%. Additionally, the voice command functionality tested achieved a 100% recognition rate, reflecting the platform’s potential to significantly ease the learning process for visually impaired students. The findings underscore the importance of integrating assistive technologies into educational platforms to ensure all students have equal access to learning opportunities.
COMPARATIVE ANALYSIS OF EXPONENTIAL SMOOTHING MODELS FOR SALES PREDICTION AND SUPPLY MANAGEMENT IN E-COMMERCE Aji Saeful; Fandy Setyo Utomo; Yuli Purwati; Mohd Sanusi Azmi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5035

Abstract

In the growing era of e-commerce, stock management is crucial. Problems arise in forecasting sales in order to achieve effective stock management. This research uses the time series analysis method by focusing on comparing the accuracy of three forecasting methods: Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), and Triple Exponential Smoothing (TES/Holt-Winter). This research provides a solution by comparing the performance of the three methods based on the Mean Absolute Error (MAE) results and prediction graphs. The goal is to determine the most accurate forecasting method using the time series analysis method with several stages, namely data preprocessing, train/test split, modeling, and performance metrics measurement. based on the test results show MAE SES 1077, DES 96, and TES (Holt-Winter) 101. Although DES has a lower MAE, TES (Holt-Winter) provides better accuracy, especially through prediction graph analysis. Holt-Winter is recognized as the most effective method in forecasting future sales, reliable for proper stock management in the dynamic e-commerce industry. This approach is expected to improve efficiency and accuracy in enterprise stock management, support the growth of online businesses, and contribute to the literature and practice of stock management. The use of time series analysis methods, especially Holt-Winter, is considered an important strategic step to optimize sales prediction, positively impact stock management, and create a competitive advantage in a growing market
Co-Authors Adam Prayogo Kuncoro Aji Saeful Al Fauzan, Lukman Alfazri, Rifa Azmi, Mohd Sanusi Bafaqih, Mohamad Aqil Bagus Adhi Kusuma Baihaqi, Wiga Maulana Berlilana Berlilana Casro Casro Dwi Krisbiantoro Ely Purnawati, Ely Esa Damayanti Fandy Setyo Utomo Gustin Setyaningsih Hanif Hidayatulloh Imung Dwi Upaya Jamie Mayliana Alyza Khoirun Nisa Limaz Morallez, Airlangga Linda Perdanawanti Mohd Sanusi Azmi Najmuddin, Faris Labib Nandang Hermanto Nikmah Trinarsih Nurfaizah Pambudi, Aldi Setia Prasetya, Dhika Adi Prasetya Pungkas Subarkah Purbo, Yevi Septiray Putranto, Banu Dwi Ria Indriyani Rizqi Aulia Widianto Rujianto Eko Saputro Sagita, Selvi Sarmini - Sarmini Sekhudin Sekhudin Shafira, Lulu Suliswaningsih Suliswaningsih, Suliswaningsih Tandi Kukuh Jati Laksono Trinarsih, Nikmah Utomo, Dadang Wahyu Wibowo, Nurma Budi Santoso Wiga Maulana Baihaqi