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All Journal Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika CogITo Smart Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Journal of Education Technology JURTEKSI Building of Informatics, Technology and Science Infotekmesin Journal of Information Systems and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Pengabdian Mitra Masyarakat (JPMM) Electronic Journal of Education, Social Economics and Technology Bulletin of Social Informatics Theory and Application CSRID Southeast Asia Journal on Open and Distance Learning
Rujianto Eko Saputro
Universitas Amikom Purwokerto
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Optimizing the Blood Donation App with Gamification Using User-Centered Design Sari, Rida Purnama; Fatudin, Arif; Saputro, Rujianto Eko; Arifudin, Dani
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.v9i1.12988

Abstract

In today's digital era, motivating the younger generation to participate in routine voluntary blood donations is a significant challenge in the health sector. This research aimed to develop a gamified application called Gamified Blood Donation (G-BlooD), designed using the User Centered Design methodology. This application integrates gamification into the blood donation process, with features including donor location information, available blood stock data, and individual donation history. Using gamification elements such as challenges, ranking boards, and emblems enhanced user interactivity and motivation. Evaluation of G-BlooD demonstrated its effectiveness in achieving this goal; it scored 75 (Grade B) on the System Usability Scale (SUS), indicating good usability, while an average total index calculation from all responses on the Likert scale of 84.125% underscored its success in motivating younger generations towards regular blood donations. These results suggest combining digital technology with gamification can encourage recurring voluntary blood donation among younger generations. This research opens avenues for further exploration into leveraging digital technology to address other public health concerns.
Revolutionizing Sustainable Public Transportation: The Go-Bus Mobile App Journey With Design Thinking Saputro, Rujianto Eko; Faturama, Rafi; Sarmini
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.v9i1.13106

Abstract

Bus Rapid Transit (BRT) has become a popular solution to address traffic congestion in Indonesia, including in Banyumas Regency. However, the supporting services provided by the BRT system still require improvement. This study focuses on designing the Go-Bus application, by integrating gamification elements to encourage the usage of Trans Banyumas. The Design Thinking method is used, encompassing the empathy, definition, ideation, prototype, and testing stages. This prototype undergoes User Satisfaction Testing and Single Ease Question (SEQ). the average score of 84.84% has been reached from the evaluation of 11 tasks by six respondents. Then, satisfaction score of 6.73 indicates Go-Bus as a user-friendly and satisfying application. This research aims to address challenges in motivating and altering user behavior to utilize public transportation. By incorporating gamification into the UI/UX design of the application, Go-Bus offers a solution that enhances user motivation, satisfaction, and encourages a shift towards public transportation usage
Application of the XGBoost Model with Hyperparameter Tuning for Industry Classification for Job Applicants Syahputra, Akhmal Angga; Rujianto Eko Saputro
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13840

Abstract

The development of technology and changes in job market dynamics have created new challenges in aligning education with industry needs. In this research, the XGBoost model with hyperparameter tuning was applied for industry classification on job applicant data taken from the Kaggle dataset LinkedIn Job Postings in 2023. This dataset consists of 23 attributes with a total of 33,085 job vacancy data points. The experimental results show that both the model without hyperparameter tuning and with GridSearchCV produce the same classification accuracy, which is 0.89 or 89%, with stable precision, recall, and F1-Score values. The best parameters found in this study are colsample_bytree = 1.0, learning_rate = 0.3, max_depth = 6, min_child_weight = 1, n_estimators = 100, and subsample = 1.0. However, cross-validation using k-fold shows a significant increase in accuracy to 0.90, or 90%. This finding confirms that the use of cross-validation can improve the performance estimation of the model more accurately and robustly by utilizing all available data for training and testing. Moreover, the implementation of cross-validation demonstrates the importance of leveraging all data points to enhance model reliability and robustness. Future research can explore alternative hyperparameter tuning methods and apply the model to larger datasets to further validate the generalizability and reliability of the XGBoost model in different application contexts. Thus, this study underscores the significance of rigorous model evaluation techniques in achieving high-performing machine learning models
Aplikasi Damai : Desain Persuasif Aplikasi Konsultasi Kesehatan Mental Berbasis Mobile Menggunakan User Centered Design Ria Indriyani; Rujianto Eko Saputro; Nia Millatul Izza; Fery Afriansyah; Hasna Salsa Dhia; Samsul Aimah; Irwansyah Munandar; Radeta Tea Makdatuang
Infotekmesin Vol 15 No 2 (2024): Infotekmesin, Juli 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i2.2204

Abstract

Mental health is health related to a person's emotional, mental and psychological condition. Anxiety disorders are conditions in which individuals experience anxiety for short periods of time or intense episodes, where this anxiety can occur for no apparent reason. Damai is a mobile-based application designed to help overcome mental health problems, especially anxiety disorders among teenagers. This research focuses on designing a user interface that involves users directly at every stage of UCD, starting from user needs, concept design, to implementation of application prototypes. The success of this research was designing a mobile-based mental health consultation application to help teenagers who suffer from mental health. This application successfully provides several features such as mental health tests, online or offline counseling and pharmacy services. In trials conducted on 6 participants, the success of the trial can be concluded that the average direct success was 100%, the misclick rate was 25%, and the average duration was 38.1 seconds.
Building Sustainable Communities: SIMARET Development for Financial Transparency with MDALC Approach Saputro, Rujianto Eko; Nanjar, Agi; safitri feriawan, Titi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14150

Abstract

The increasing need for financial transparency and efficiency in community-level governance, particularly within Rukun Tetangga (RT) in Indonesia, calls for innovative solutions. This study presents the development of SIMARET, a mobile application designed to enhance the management of RT financial activities and resident participation, using the Mobile Application Development Life Cycle (MDALC) approach. The research aims to address the challenges of manual financial management, such as lack of transparency and difficulties in tracking funds and activities like neighborhood watch (Siskamling). SIMARET incorporates key features such as digital tracking of resident contributions (jimpitan), QR code-based attendance for Siskamling, and automated financial reports. The system was developed through MDALC’s structured phases: identification, design, development, testing, and deployment. Blackbox Testing and User Acceptance Testing (UAT) were conducted to ensure functionality and user satisfaction. The results show a high satisfaction rate of 97%, confirming that SIMARET simplifies financial administration and enhances community participation. The study also highlights the application’s contribution to the United Nations Sustainable Development Goals (SDG) 16 by promoting transparency and effective governance at the local level. Although SIMARET demonstrates significant potential, further research is recommended to improve its user interface design and expand its implementation in other communities.
Machine Learning and Deep Learning Approaches for Energy Prediction: A Systematic Literature Review Nanjar, Agi; Saputro, Rujianto Eko; Berlilana, Berlilana
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14208

Abstract

This paper offers a literature review on the application of Machine Learning (ML) and Deep Learning (DL) techniques in energy prediction. Contemporary energy systems' challenges, such as load fluctuations and uncertainties linked to renewable energy sources, render traditional methods like ARIMA and linear regression insufficient. The objective of this paper is to identify the most widely used ML and DL approaches, compare their performance against conventional methods, and explore the implementation challenges along with potential solutions. The methodology for this literature review involves analyzing publications from Scopus, IEEE Xplore, and ScienceDirect covering the period from 2019 to 2024. The findings indicate that DL methods, particularly Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, are effective in handling sequential data, while hybrid models like CNN-GRU enhance prediction accuracy in innovative grid applications. Challenges identified include overfitting and data complexity, which can be addressed through regularization techniques and computational optimization using GPUs. In conclusion, this paper asserts that ML and DL play a significant role in improving prediction accuracy and facilitating the transition towards sustainable energy and smart grids. To further enhance performance in the future, the paper recommends the development of ensemble models and the integration of attention mechanisms.
Novel Predictive Framework for Student Learning Styles Based on Felder-Silverman and Machine Learning Model Maulana Baihaqi, Wiga; Eko Saputro, Rujianto; Setyo Utomo, Fandy; Sarmini, Sarmini
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.408

Abstract

This study analyzes data from the Open University Learning Analytics Dataset to evaluate how students' interactions with Virtual Learning Environment (VLE) materials influence their final outcomes. This research aims to formulate and build a novel predictive framework based on the Felder-Silverman and Machine Learning Model for student learning styles. Based on these objectives, this research provides novelty and contributions since it enhances student data analysis, uses a learning model using Felder-Silverman Learning Style Model (FSLSM) to give a more comprehensive understanding of students' learning styles, and improves prediction accuracy by introducing Artificial Neural Network (ANN) and feature selection using Random Forest. The data used includes 3 main files: vle.csv, which contains information about the materials and activities in the VLE; studentVle.csv, which records students' interactions with the materials; and studentInfo.csv, which provides demographic information of students and their final outcomes. The analysis process involved data merging and processing, including handling of missing values, data type conversion, as well as mapping activity types to learning style features based on the FSLSM. We use the Random Forest feature selection method, as well as data imbalance handling techniques such as oversampling, to improve model performance. The applied classification models include Logistic Regression, K-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), and ANN. The analysis results showed that after tuning, the Random Forest model achieved 97% accuracy, while SVM achieved 97% accuracy as well, with better performance than previous studies. This research highlights the importance of comprehensive data integration and appropriate processing techniques in improving the accuracy of student learning style prediction. Based on the increase in accuracy results, it can be beneficial for more effective personalized learning and improve our understanding of students' learning style preferences. The research advances knowledge and provides practical applications for educators to tailor their teaching strategies.
An efficient and interactive android-based neighborhood management Junianto, Haris; Saputra , Dhanar Intan Surya; Saputro, Rujianto Eko
Bulletin of Social Informatics Theory and Application Vol. 8 No. 2 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i2.681

Abstract

This research aims to design and develop a prototype Android-based Neighborhood Association management information system application using the Agile approach to assist Neighborhood Association administrators in real-time administrative processes and information dissemination. The Agile approach was selected to enhance flexibility and responsiveness in application development, enabling adjustments to potential user needs and changes that may occur during the development process. The application is expected to improve service quality and governance transparency at the Neighborhood Association level while facilitating residents' access to information and interaction with Neighborhood administrators. The application development process employs the Agile approach, involving the development team in iterative cycles to meet user requirements. Research results demonstrate the achievement of research objectives, with the application capable of managing resident data, Neighborhood Association finances, event scheduling, and Neighborhood Association news. The Agile approach used in the application's development provides the flexibility needed to adapt to changing user requirements, offering a solution to the challenges faced by Neighborhood Association administrators in performing their duties. This aligns with Agile principles, emphasizing user collaboration and responsiveness to changes.
Eksplorasi Sentimen Publik terhadap Film "˜Dirty Vote"™ melalui Metode Naïve Bayes dan Logistic Regression Junianto, Haris; Saputro, Rujianto Eko; Kusuma, Bagus Adhi; Saputra, Dhanar Intan Surya
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 10, No 3 (2024): Volume 10 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v10i3.78520

Abstract

Tahun 2024 merupakan tahun politik bagi masyarakat Indonesia, di mana mereka menggunakan hak pilih untuk menentukan pemimpin pemerintahan selama lima tahun ke depan. Dalam konteks ini, pendidikan politik menjadi sangat penting, terutama bagi warga yang kurang memahami seluk-beluk politik dan proses pemilihan umum. Menyadari pentingnya pemahaman tersebut, sekelompok akademisi menciptakan film berjudul "Dirty Vote" dengan tujuan meningkatkan kesadaran masyarakat mengenai proses pemilu serta meminimalisir potensi pelanggaran.Penelitian ini bertujuan untuk mengevaluasi opini publik terkait film "Dirty Vote" dengan menggunakan dua model klasifikasi, yaitu Naive Bayes dan Logistic Regression. Penelitian ini melibatkan beberapa tahap, mulai dari pengumpulan data melalui scraping komentar dari platform YouTube, preprocessing data, analisis eksploratif (Exploratory Data Analysis), hingga pengujian performa model menggunakan teknik K-fold Cross Validation, serta visualisasi data menggunakan Word Cloud. Dalam penelitian ini, sebanyak 8888 data komentar dianalisis menggunakan teknik pemrosesan bahasa alami untuk mengukur sentimen publik terhadap film tersebut. Hasil analisis menunjukkan bahwa algoritma Naive Bayes mengidentifikasi 91,30% sentimen positif dan 8,70% sentimen negatif, sedangkan algoritma Logistic Regression memberikan hasil yang lebih tinggi, dengan sentimen positif sebesar 95,65% dan negatif sebesar 4,35%. Dari segi performa, Logistic Regression terbukti lebih unggul dengan akurasi mencapai 95,5%, sedangkan Naive Bayes memiliki akurasi sebesar 91,1%. Pengujian performa dilakukan melalui satu kali pengujian penuh serta delapan kali pengujian dalam berbagai kondisi data, dengan evaluasi kinerja menggunakan ROC dan AUC. Hasil penelitian ini menunjukkan bahwa kedua algoritma memberikan evaluasi positif terhadap film "Dirty Vote", dengan Logistic Regression memberikan hasil yang lebih akurat.
COMPARISON OF LOGISTIC REGRESSION AND RANDOM FOREST IN SENTIMENT ANALYSIS OF DISDUKCAPIL APPLICATION REVIEWS Junianto, Haris; Saputro, Rujianto Eko; Kusuma, Bagus Adhi; Saputra, Dhanar Intan Surya
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.1802

Abstract

Civil registration administration institutions such as Disdukcapil have an important role in carrying out government functions, in supporting the smooth running of administrative services the Government presents the Disdukcapil Mobile Application platform which aims to provide efficient and fast services to the community regarding various population administration needs. Sentiment analysis of user reviews on the Play Store for the Disdukcapil application is needed to understand user perceptions and needs, as well as to improve service quality and application development. In this study, researchers conducted sentiment analysis using 2 algorithms, namely: Logistic Regression and Random Forest, which after comparing by testing the two algorithms with test data of 18810 user review data from PlayStore, obtained the performance results of each algorithm as follows: 90% accuracy, 91% precision, 89% recall, and f1 90% for the performance results of the Logistic Regression algorithm, while for the performance results of the Random Forest algorithm accuracy 89%, precision 92%, recall 86% and f1-score 89%. From these results the Logical Regression algorithm has better performance than the Random Forest algorithm.
Co-Authors Adam Prayogo Kuncoro Adam Prayogo Kuncoro Adiatma, Febriansyah Husni Adiya, Az Zahra Dwi Nur Afriansyah, Fery Aimah, Samsul Arif Mu'amar Wahid Aulia Hamdi Azhari Shouni Barkah Bagaskoro, Galih Berlilana Berlilana Cahyo, Samsul Dwi Chyntia Raras Ajeng Widiawati Damayanti, Wenti Risma Dani Arifudin Darmono Deasy Komarasary Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Ely Purnawati Ely Purnawati, Ely Embong Octavianto Fandy Setyo Utomo Fatudin, Arif Faturama, Rafi Febrianti, Diah Ratna Fery Afriansyah Filanzi, Shendy Giat Karyono Hasna Salsa Dhia hidayatulloh, hanif Ikmah Ikmah Ikmah, Ikmah Ilham, Rifqi Arifin Indriyani, Ria Irwansyah Munandar Ismail, Dimas Shafa Malik Junianto, Haris Kusuma, Bagus Adhi Latif, Imam Sofarudin Lughri Wijaya Pamungkas Maharani, Revalyna Octavia Maulana Baihaqi, Wiga Millatul Izza, Nia Mohd. Hafiz Zakaria Munandar, Irwansyah Nanjar, Agi Ndari, Arum Vika Nia Millatul Izza Novita Eka Ramadhani Nurfaizi, Maulana Octavianto, Embong Pandu W, Muhammad Arfianto Prasetyo, Agung Pungkas Subarkah Purwadi Purwadi Putranto, R. Vitto Mahendra Radeta Tea Makdatuang Ramadhan, Rio Fadly Ria Indriyani Rizqi Aulia Widianto Rohmah, Umdah Aulia Rosana Fadila Sari safitri feriawan, Titi Salam, Sazilah Salsa Dhia, Hasna Samsul Aimah Saputra , Dhanar Intan Surya Saputra, Alfin Nur Aziz Saputri, Inka Sari, Rida Purnama Sarmini Sarmini - Sarmini Sarmini Sarmini Sazilah Salam Serli, Serli Sofa, Nur Sri Hartini Subarkah, Pungkas Suliswaningsih, Suliswaningsih Syahputra, Akhmal Angga Tanzilla, Armeyta Putri Tarwoto, T Tea Makdatuang, Radeta Titi Safitri Maharani Toni Anwar Turino, Turino Wahyuni, Irmawati Tri Wenti Risma Damayanti Wiga Maulana Baihaqi Wijaya, Anugerah Bagus Yuli Purwati Yulianto, Koko Edy