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All Journal Telematika JUITA : Jurnal Informatika Paradigma CogITo Smart Journal Matrix : Jurnal Manajemen Teknologi dan Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) ILKOM Jurnal Ilmiah Reswara: Jurnal Pengabdian Kepada Masyarakat Rambideun : Jurnal Pengabdian Kepada Masyarakat Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Southeast Asia Journal on Open and Distance Learning
Wiga Maulana Baihaqi
Universitas Amikom Purwokerto
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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Analisis Performa Algoritma Klasifikasi pada Sentimen Ulasan Pengguna terhadap Aplikasi Muamalat DIN: Analisis Performa Algoritma Klasifikasi pada Sentimen Ulasan Pengguna terhadap Aplikasi Muamalat DIN Wiga Maulana Baihaqi; Ika Romadoni Yunita; Aulia Shafira Tri Damayanti; Luthfi Akhaerunnisa
CogITo Smart Journal Vol. 9 No. 2 (2023): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v9i2.511.241-251

Abstract

Banking applications have become an integral part of modern society. One such application is Muamalat DIN, launched by Bank Muamalat Indonesia with the aim of facilitating customers in conducting various transactions and activities. User reviews of this application vary widely, ranging from positive to negative comments. The purpose of this study is to evaluate user attitude on reviews of Bank Muamalat Indonesia's digital banking product, the Muamalat DIN application. This research offers insights into the efficacy of the SMOTE balancing technique compared to undersampling by utilizing a methodology that includes data collection via scrapping techniques, data preprocessing, and the application of Multi Layer Perceptron (MLP), XGBoost, and LightGBM classification algorithms. The results show that SMOTE-paired XGBoost works better for sentiment categorization. The study's conclusion emphasizes the significance of choosing the right data balancing method to increase sentiment analysis's accuracy in Islamic banking applications, which can be used as a foundation for strategies aimed at enhancing customer service and making decisions.
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.
IMPROVING STUNTING CLASSIFICATION PERFORMANCE USING COMBINATION SMOTE TECHNIQUE AND ARTIFICIAL NEURAL NETWORK ALGORITHM Wiga Maulana Baihaqi; Ida Nur Laela; Darso Darso
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.v10i1.4998

Abstract

Child development is at the core of the nation's future. However, there are still serious problems that hinder optimal child growth, one of which is stunting. Stunting is a condition that has become a global concern in the context of public health and development. The use of deep learning algorithms has great potential to overcome the problem of stunting classification. The ratio of stunting handling is still a problem due to imbalance data. Classification algorithms such as ANN will experience a decrease in performance when faced with unbalanced classes, this makes it difficult to take action for early diagnosis of stunting. Synthetic Minority Oversampling Technique (SMOTE) is used to balance the failure data in diagnosis. The results showed that the combination of the SMOTE oversampling technique can improve the ability of the ANN algorithm model to accurately classify stunted or minority populations. The accuracy, precision, recall, and F1-Score values of this study are 0.90, 0.85, and 0.95, respectively. The results of (MCC) obtained a value of 0.73, and (G-Mean) of 0.86 before applying SMOTE and the results after applying SMOTE MCC of 0.84 and G-Mean of 0.92. This indicates that the minority group, namely stunted toddlers, can be predicted well. The implementation of the combination of SMOTE and ANN has proven successful in classifying imbalance stunting data, so this research can be used as a reference for future research to handle unbalanced data.
Effectiveness of The Gamified LMS Platform on The Level of Online Course Completion Rujianto Eko Saputro; Wiga Maulana Baihaqi; Sarmini
Southeast Asian Journal on Open and Distance Learning Vol. 1 No. 01 (2023): Strategies for Cultivating Active Learning in Online Environment
Publisher : SEAMEO SEAMOLEC

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

Abstract

Gamified Massive Open Online Courses (G-MOOCs) is a Learning Management System (LMS) platform built on the gamification framework (MARC Gamification Framework) that has been proposed in previous studies based on various aspects of game elements, social learning, motivation and interactive theory learning environment (ILE). G-MOOC is a background element that can motivate them when taking courses in online learning. This program is intended to increase the intrinsic motivation of participants in completing their courses. Tests are carried out using the experimental group method using two indicators, namely the level of mastery of the course (performance) and the status of learning courses (Done/Not Done). To produce data from used indicators, researchers gave four weeks to take the course. The courses are compared with the LMS platform which has no gamification element (SIMOOC), the performance indicators are tested in the participant values between the G-MOOCs platform and the SIMOOC platform. Based on the results of the test, the platform is a platform that is better and different compared to the SIMOOC platform. Judging from the status of the participants, out of 71 participants who took the course on the G-MOOC platform there were 46.5% which were declared completed, while on the SIMOOC platform only 7% had completed status. It can be concluded that the G- MOOC platform can increase the effectiveness of the value courses compared to the SIMOOC platform.
Co-Authors Afnatasya Dwias Putri Anjar Wati Arif Munandar Atika Nurlaeli Aulia Shafira Tri Damayanti Azhar Fadholi Bagus Adhi Kusuma Berlilana Berlilana Christoper Prima Chyntia Raras Ajeng Widiawati Darso Darso Dila Putri Sabil Dwi Ayu Mutiara Fandy Setyo Utomo Fatma Sulistiyana Fina Ajril Izzati Ida Nur Laela Ika Romadoni Yunita Kuat Indartono Luthfi Akhaerunnisa Muh Nursaddam Muhammad Luqman Nabella Putri Widianto Nurhopipah, Ade Rinda Utami Rizky Adhytia Rujianto Eko Saputro Sarmini - Sarmini Sitaresmi Wahyu Handani Syifaul Banat Yuli Purwati