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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Teknologi Informasi dan Ilmu Komputer Telematika JUITA : Jurnal Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIN (Jurnal Online Informatika) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Sebatik Journal of Computer Science and Informatics Engineering (J-Cosine) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Mnemonic International Journal of Advances in Data and Information Systems Computer Science and Information Technologies Jurnal Teknik Informatika (JUTIF) Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Jurnal Pengabdian Masyarakat Tekno Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Friska Abadi
Universitas Lambung Mangkurat
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Library & Information Science Materials Science & Nanotechnology

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Pengembangan Aplikasi Edukasi Pengenalan Pohon Berbasis Qr Code Scanner Friska Abadi; Mohammad Reza Faisal; Radityo Adi Nugroho
Jurnal Pengabdian kepada Masyarakat TEKNO (JAM-TEKNO) Vol 4 No 2 (2023): Desember 2023
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/jamtekno.v4i2.5577

Abstract

Natural tourist areas certainly have a variety of biodiversity that can be used as a place for education. Many types of trees can be found in the area, such as Beringin, Jeruju, Jingah, Nipah, and so on. However, the collection of data about trees has not been properly recorded and there is no comprehensive information about the trees there, so that when visitors travel there it will be difficult to identify them. Therefore, in an effort to help overcome existing problems, it is necessary to develop technology with QR Codes to be able to identify trees in natural tourism parks. The stages of implementing the service are creating an application where this application is made in the form of a website-based information system and socializing the use of the tree recognition educational application. The goal to be achieved is to create a website-based application for recognizing trees, so that visitors, whether they come directly or just want to see the tree collection in the natural tourism park but are constrained by distance, time, etc., visitors can access via a web browser on smartphones, laptops, etc. and personal computers wherever they are provided there is an internet connection.
Pengembangan Sistem Manajemen Sarana Dan Prasarana, IT, Serta Laboratorium Di SMK Telekomunikasi Putri Nabella; Rudy Herteno; Setyo Wahyu Saputro; Friska Abadi; Muhammad Itqan Mazdadi; Nabella, Putri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 1: Februari 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025128649

Abstract

Bidang Sarana dan Prasarana, IT, serta Laboratorium di SMK Telekomunikasi menghadapi tantangan dalam pengelolaan data yang tersebar di berbagai file Microsoft Excel, menyebabkan kesulitan dalam pengumpulan laporan untuk audit dan sertifikasi. Penelitian ini bertujuan mengembangkan sistem manajemen terpadu menggunakan framework CodeIgniter 4, PHP, dan MySQL dengan metode Rational Unified Process (RUP) dan desain Unified Modelling Language (UML). Sistem ini dirancang untuk menyelaraskan pengelolaan data dan memfasilitasi penyajian informasi yang efisien. Hasil pengujian black box menunjukkan tingkat keberhasilan 100%, sementara user acceptance testing memperoleh skor 92% dengan predikat sangat baik. Implementasi sistem ini diharapkan meningkatkan efisiensi dan efektivitas manajemen sarana, prasarana, IT, dan laboratorium di SMK Telekomunikasi, memberikan kontribusi signifikan terhadap peningkatan kualitas pengelolaan dan kepuasan pengguna.   Abstract. The Facilities and Infrastructure, IT, and Laboratory Department at SMK Telekomunikasi faces challenges in managing data scattered across various Microsoft Excel files, resulting in difficulties in compiling reports for audits and certifications. This research aims to develop an integrated management system using the CodeIgniter 4 framework, PHP, and MySQL, employing the Rational Unified Process (RUP) methodology and Unified Modelling Language (UML) design. This system is designed to streamline data management and facilitate efficient information presentation. The results of the black box testing showed a success rate of 100%, while the user acceptance testing scored 92% with an excellent rating. The implementation of this system is expected to enhance the efficiency and effectiveness of managing facilities, infrastructure, IT, and laboratories at SMK Telekomunikasi, significantly contributing to improved management quality and user satisfaction.
Implementation of PPCA Imputation, SMOTE-N Class Balancing in Hepatitis Classification Using Naïve Bayes Fathmah, Siti; Kartini, Dwi; Abadi, Friska; Budiman, Irwan; Mazdadi, Muhammad Itqan
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21528

Abstract

The availability of complete data in research is crucial, especially in the initial stages. The Hepatitis data used in this study encountered issues such as missing data and class imbalance, which hindered its optimal utilization. The method employed to address missing data was the PPCA imputation method. After filling in the missing data, the data was balanced using the SMOTE-N class balancing method and classified using Gaussian Naïve Bayes. The aim of this research was to compare the classification evaluation of hepatitis disease using Naive Bayes with the PPCA imputation approach and SMOTE-N class balancing. The best results from each scenario yielded an AUC value of 0.833 in the first scenario with an 80:20 data split for training and testing, and 0.875 in the second scenario with a 90:10 data split. The highest AUC value was obtained in the application of PPCA imputation with SMOTE-N class balancing using Naive Bayes classification. This demonstrates that the implementation of PPCA imputation with SMOTE-N class balancing has a better impact on the performance of Naïve Bayes classification.
Newspaper Ad Submission and Payment Website Measurement Analysis Using McCall and PIECES Muhammad Nazar Gunawan; Friska Abadi; Dodon Turianto Nugrahadi; Irwan Budiman; Setyo Wahyu Saputro
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30355

Abstract

The transition to digital platforms in the media industry requires robust systems to ensure efficiency and user satisfaction. As with Digital Iklan Radar Banjarmasin, the Newspaper ad submission and payment website, there is a need for evaluation to comprehensively ensure software feasibility and quality. This research evaluates the quality of the Newspaper ad submission and payment website using the McCall and PIECES frameworks, comparing their strengths and identifying areas for improvement. This research contributes to determining the most suitable evaluation methods for such types of websites while offering actionable insights for developers to improve the quality of systems and services. Data collection involved online surveys with 106 respondents and 38 Likert-scale questions mapped to McCall and PIECES frameworks. Statistical tests, including validity, reliability, and an independent t-test, were applied to compare results. McCall's evaluation rated the system at 68% (Good), with low scores in Usability (38.5%), Reliability (36.77%), and Efficiency (38.15%), indicating areas needing significant improvement. PIECES evaluation scored 80.4% (Good), with Performance (81%) and Service (82.39%) rated Very Good, though Control and Security (78.55%) required enhancement. Statistical analysis with independent t-test confirmed significant differences between the two methods, indicating that both methods measure aspects of software quality from different perspectives, thus providing complementary insights for evaluation. The study highlights the complementary nature of McCall and PIECES in software quality evaluation. Recommendations include improving usability, system stability, and security for better user experiences. Future research should involve broader demographic samples and different system types to validate findings and enhance generalizability.
A Cost-Effective Vital Sign Monitoring System Harnessing Smartwatch for Home Care Patients Dodon Turianto Nugrahadi; Rudy Herteno; Mohammad Reza Faisal; Nursyifa Azizah; Friska Abadi; Irwan Budiman; Muhammad Itqan Mazdadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5126

Abstract

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to relatively small cell sizes and overlapping cell nuclei. Therefore, an accurate analysis of the Pap smear image is essential to obtain the right information. This research compares nucleus segmentation and detection using gray-level cooccurrence matrix (GLCM) features in two methods: Otsu and polynomial. The data tested consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate characteristics. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for future studies in developing new methods to enhance identification accuracy.
Game Development of Banjar Archive for Interactive Cultural Education Ultilizing Large Language Models Adi Mu'Ammar, Rifqi; Abadi, Friska; Budiman, Irwan; Adi Nugroho, Radityo; Turianto Nugrahadi, Dodon
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2294

Abstract

The preservation of Banjar cultural heritage is threatened by globalization and the fading interest of younger generations. This study addressed these challenges by developing an interactive educational game using the Game Development Life Cycle (GDLC) framework and integrating Large Language Models (LLMs) for adaptive and immersive player interactions. The six stages of GDLC namely initiation, pre-production, production, testing, beta, and release were systematically applied, resulting in a game that blends dynamic narratives to engage players while educating them about Banjar culture. Black Box Testing verified 14 test scenarios that all passed successfully, ensuring system stability and reliability. Additionally, user experience evaluation using the Game Experience Questionnaire (GEQ) highlighted high levels of immersion (4.936), competence (4.448), flow (3.124) and positive affect (4.976) among players, with minimal reported tension (1), challenge (1.744) and negative affect (1.07). These results demonstrated that the game successfully balances educational goals with engaging gameplay, fostering meaningful connections to Banjar heritage. By leveraging LLM technology, the game enhances interactivity, offering an innovative approach to Banjar cultural preservation in the digital era. This research extends the existing body of knowledge on AI-driven gamification strategies in heritage conservation with a specific focus on Banjar culture.
Seleksi Fitur dengan Particle Swarm Optimization pada Klasifikasi Penyakit Parkinson Menggunakan XGBoost Kurnia, Deni; Itqan Mazdadi, Muhammad; Kartini, Dwi; Adi Nugroho, Radityo; Abadi, Friska
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107252

Abstract

Penyakit Parkinson merupakan gangguan pada sistem saraf pusat yang mempengaruhi sistem motorik. Diagnosis penyakit ini cukup sulit dilakukan karena gejalanya yang serupa dengan penyakit lain. Saat ini diagnosa dapat dilakukan menggunakan machine learning dengan memanfaatkan rekaman suara pasien. Fitur yang dihasilkan dari ekstraksi rekaman suara tersebut relatif cukup banyak sehingga seleksi fitur perlu dilakukan untuk menghindari memburuknya kinerja sebuah model. Pada penelitian ini, Particle Swarm Optimization digunakan sebagai seleksi fitur, sedangkan XGBoost akan digunakan sebagai model klasifikasi. Selain itu model juga akan diterapkan SMOTE untuk mengatasi masalah ketidakseimbangan kelas data dan hyperparameter tuning pada XGBoost untuk mendapatkan hyperparameter yang optimal. Hasil pengujian menunjukkan bahwa nilai AUC pada model dengan seleksi fitur tanpa SMOTE dan hyperparameter tuning adalah 0,9325, sedangkan pada model tanpa seleksi fitur hanya mendapat nilai AUC sebesar 0,9250. Namun, ketika kedua teknik SMOTE dan hyperparameter tuning digunakan bersamaan, penggunaan seleksi fitur mampu memberikan peningkatan kinerja pada model. Model dengan seleksi fitur mendapat nilai AUC sebesar 0,9483, sedangkan model tanpa seleksi fitur hanya mendapat nilai AUC sebesar 0,9366.   Abstract   Parkinson's disease is a disorder of the central nervous system that affects the motor system. Diagnosis of this disease is quite difficult because the symptoms are similar to other diseases. Currently, diagnosis can be done using machine learning by utilizing patient voice recordings. The features generated from the extraction of voice recordings are relatively large, so feature selection needs to be done to avoid deteriorating the performance of a model. In this research, Particle Swarm Optimization is used as feature selection, while XGBoost will be used as a classification model. In addition, the model will also be applied SMOTE to overcome the problem of data class imbalance and hyperparameter tuning on XGBoost to get optimal hyperparameters. The test results show that the AUC value on the model with feature selection without SMOTE and hyperparameter tuning is 0.9325, while the model without feature selection only gets an AUC value of 0.9250. However, when both SMOTE and hyperparameter tuning techniques are used together, the use of feature selection is able to provide improved performance on the model. The model with feature selection gets an AUC value of 0.9483, while the model without feature selection only gets an AUC value of 0.9366.
Design of Application Framework for Vital Monitoring Mobile-Based System Rizky Ananda, Muhammad; Faisal, Mohammad Reza; Herteno, Rudy; Nugroho, Radityo Adi; Abadi, Friska
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28416

Abstract

In the realm of modern healthcare, continuous monitoring can leverage the affordable wearable devices available on the market to manage costs. However, these devices face several limitations, such as restricted access for other parties, including nurses and doctors, and the need for redevelopment to integrate new devices for data accessibility. This study addresses these challenges by establish an application framework tailored for mobile-based systems, by ensuring accessibility by external parties. The research contribution is encompassing two key aspects: the potential implementation of Feature-Oriented Domain Analysis (FODA) in the domain of mobile-based vital sign monitoring, particularly in the absence of prior studies addressing the same context, and the identification reusable (frozen spots) and adaptable components (hot spots), providing guidance for the development of mobile-based vital sign monitoring. FODA is utilized during the analysis activity. Design patterns are then implemented when creating class diagrams in the design activity. This study finding reveal 7 primary features and 18 sub-features essential that must be incorporated into the application framework. The framework includes 5 hot spots and 7 frozen spots, with the implementation of Strategy and Filter design patterns. In conclusion, the developed application framework represents a significant advancement in vital sign monitoring, particularly within mobile-based systems. This study emphasizing limitations in analysis and design phases. In future research, the focus will shift to the construction and stabilization phases, all crucial for refining the framework. Implementing framework in actual applications can aid in developing vital sign monitoring systems and potentially improving healthcare outcomes.
Effect of SMOTE Variants on Software Defect Prediction Classification Based on Boosting Algorithm Aflaha, Rahmina Ulfah; Herteno, Rudy; Faisal, Mohammad Reza; Abadi, Friska; Saputro, Setyo Wahyu
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28521

Abstract

Detecting software defects early on is critical for avoiding significant financial losses. However, building accurate software defect prediction models can be challenging due to class imbalance, where the data for defective modules is much less than for standard modules. This research addresses this issue using the imbalanced dataset NASA MDP. To address this issue, researchers have proposed new methods that combine data level balancing approaches with 14 variations of the SMOTE algorithm to increase the amount of defective module data. An algorithm-level approach with three boosting algorithms, Catboost, LightGBM, and Gradient Boosting, is applied to classify modules as defective or non-defective. These methods aim to improve the accuracy of software defect prediction. The results show that this new method can produce a more accurate classification than previous studies. The DSMOTE and Gradient Boosting pair with 0.9161 has the highest average accuracy (0.9161). The DSMOTE and Catboost model achieved the highest average AUC value (0.9637). The ADASYN kernel and Catboost showed the best ability to perform the average G-mean value (0.9154). The research contribution to software defect prediction involves developing new techniques and evaluating their effectiveness in addressing class imbalance.
Improving with Hybrid Feature Selection in Software Defect Prediction Pratama, Muhammad Yoga Adha; Herteno, Rudy; Faisal, Mohammad Reza; Nugroho, Radityo Adi; Abadi, Friska
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1307

Abstract

Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. So this research focuses on improving PSO performance by using feature selection methods with hybrid techniques to overcome these problems. The feature selection techniques used are Filter and Wrapper. The methods used are Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Forward Selection (FS) because feature selection methods have been proven to overcome data dimensionality problems and eliminate noisy attributes. Feature selection is often used by some researchers to overcome these problems, because these methods have an important function in the process of reducing data dimensions and eliminating uncorrelated attributes that can cause noisy. Naive Bayes algorithm is used to support the process of determining the most optimal class. Performance evaluation will use AUC with an alpha value of 0.050. This hybrid feature selection technique brings significant improvement to PSO performance with a much lower AUC value of 0.00342. Comparison of the significance of AUC with other combinations shows the value of FS PSO of 0.02535, CFS FS PSO of 0.00180, and CS FS PSO of 0.01186. The method in this study contributes to improving PSO in the SDP domain by significantly increasing the AUC value. Therefore, this study highlights the potential of feature selection with hybrid techniques to improve PSO performance in SDP.
Co-Authors A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA Abdullayev, Vugar Achmad Zainudin Nur Adi Mu'Ammar, Rifqi Aflaha, Rahmina Ulfah Ahmad Juhdi Alfando, Muhammad Alvin Amalia, Raisa Andi Farmadi Andi Farmandi Arif, Nuuruddin Hamid Athavale, Vijay Anant budiman, irwan Deni Kurnia Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Emma Andini Faisal, Mohammad Reza Fathmah, Siti Fatma Indriani Fauzan Luthfi, Achmad Febrian, Muhamad Michael Halimah Halimah Halimah Herteno, Rudy Herteno, Rudy Indriani, Fatma Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Kartika, Najla Putri M Kevin Warendra Mafazy, Muhammad Meftah Martalisa, Asri Maulana, Muhammad Rafly Alfarizqy Mera Kartika Delimayanti Muhamad Fawwaz Akbar Muhammad Adika Riswanda Muhammad Alkaff Muhammad Azmi Adhani Muhammad Denny Ersyadi Rahman Muhammad Fikri Muhammad Haekal Muhammad Itqan Mazdadi Muhammad Khairin Nahwan Muhammad Mirza Hafiz Yudianto Muhammad Nazar Gunawan Muhammad Noor Muhammad Reza Faisal, Muhammad Reza Muhammad Sholih Afif Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Nabella, Putri Nor Indrani Nugrahadi, Dodon Nurlatifah Amini Nursyifa Azizah Prastya, Septyan Eka Pratama, Muhammad Yoga Adha Putri Nabella Raditya, Virgi Atha Radityo Adi Nugroho Rahman Hadi Rahman Rahmat Ramadhani Rahmawati, Nanda Putri Rahmayanti Rahmayanti Ramadhan, Muhammad Rizky Aulia Reina Alya Rahma Rezeki, Abdillah Rinaldi Riza Susanto Banner Rizal, Muhammad Nur Rizky Ananda, Muhammad Rizky, Muhammad Hevny Rudy Herteno SALLY LUTFIANI Saputro, Setyo Wahyu Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sarah Monika Nooralifa Sa’diah, Halimatus Septyan Eka Prastya Setyo Wahyu Saputro Siti Napi'ah Tri Mulyani Ulya, Azizatul Umar Ali Ahmad Vina Maulida, Vina Wahyu Dwi Styadi Wahyu Saputro, Setyo Yunida, Rahmi