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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) Teknika Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer CESS (Journal of Computer Engineering, System and Science) SemanTIK : Teknik Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika Paradigma JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Informatics for Educators and Professional : Journal of Informatics INOVTEK Polbeng - Seri Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jusikom : Jurnal Sistem Komputer Musirawas JURTEKSI Multitek Indonesia : Jurnal Ilmiah Jurnal Ilmiah Sinus Jurnal Informatika Kaputama (JIK) JSAI (Journal Scientific and Applied Informatics) Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) JIKA (Jurnal Informatika) Infotek : Jurnal Informatika dan Teknologi Teknika Journal of Electrical Engineering and Computer (JEECOM) Jurnal Computer Science and Information Technology (CoSciTech) Media Jurnal Informatika JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Ilmiah Edutic : Pendidikan dan Informatika NERO (Networking Engineering Research Operation) Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science
Taufik Rahman
Universitas Bina Sarana Informatika
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Other

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Application of the Technological Acceptance Model(TAM) Approach to the Influence of Public Perceptions Using Digital Wallets Saputra, Elin Panca; Saputro, Achmat Yulyadi; Priyono, Priyono; Kusumo, Aryo Tunjung; Rahman, Taufik
Telematika Vol 21 No 1 (2024): Edisi Pertama 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i1.12083

Abstract

Purpose: This research aims to conduct a study of the perceptions of digital wallet users and how users' reactions influence the benefits of digital wallets in the big city of JakartaDesign/methodology/approach: The research method applied is a quantitative method. The population of this research is digital wallet users in the city of Jakarta. The number of samples applied was 121 respondents according to the purposeful sampling method. The data testing methods applied are convergent validity, discriminant validity, composite reliability and Cronbanch alpha. Data calculations apply Smart PLS 3 software. The results of this study show that trust and perceived risk do not influence user preferences for using digital walletsFindings/result: The results of this research constantly support a number of previous studies related to TAM where perceived usefulness and perceived ease of use play a direct and indirect role in interest in using digital wallets. So the community's perceived usefulness is a variable that has a prominent influence on the preferences of digital wallet users in the city of Jakarta.Originality/value/state of the art: The steps taken from the start of the study to its conclusion were designed to use the TAM approach to determine how the public felt about this particular study. This study uses quantitative research methods and yields two models: an inner model, or structural model, that includes path analysis through Smart PLS 3 data analysis, and an outer model, or measurement model, that includes composite reliability, conbranch alpha, discriminant validity, and convergent validity. 
Penerapan K-Means untuk Pengelompokan Hasil Belajar Informatika Rahman, Taufik; Ahmad Sahroni, Abdul
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4556

Abstract

This study was conducted at SMK Kesehatan Prima Indonesia, which integrates informatics subjects into its curriculum, although the main focus is on developing students' health competencies. The main challenge faced is the management of informatics learning outcomes, especially in grouping students based on their understanding and achievements, which has been done manually and inefficiently. This study aims to identify groups of student learning outcomes using the K-Means Clustering method and describe the differences between the groups formed. This study is an exploratory study with a quantitative approach. The results of the clustering analysis on grade 10 students showed the formation of three groups: high score groups (24 students), medium score groups (51 students), and low score groups (42 students). In addition, learning interest and use of IT devices were shown to have a significant influence on informatics learning outcomes. These findings confirm that the application of the K-Means algorithm can improve the effectiveness of teaching strategies by grouping students in a more structured manner based on their learning outcomes.    
EVALUASI USABILITY APLIKASI EMPLOYEE SELF SERVICE (ESS) PADA PT. TOYOTA BOSHOKU INDONESIA MENGGUNAKAN METODE USABILITY TESTING DAN SYSTEM USABILITY SCALE Rahman, Taufik; Hadi Candra, Tri; Agus Sobari, Irwan
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13740

Abstract

Perkembangan teknologi yang cepat terutama dalam sektor aplikasi mobile, telah menghasilkan banyak aplikasi yang digunakan untuk mempermudah berbagai aktivitas manusia, bahkan menjadikannya sebagai kebutuhan dasar dalam kehidupan sehari-hari. Namun, aplikasi mobile dengan antarmuka pengguna yang kompleks dapat menyebabkan ketidaknyamanan bagi pengguna dan berpotensi memicu kesalahan selama penggunaan. Tujuan dalam penelitian yaitu: 1. Mengetahui hasil evaluasi tingkat learnability, efficiency, dan error pada interface ESS menggunakan Usability Testing Method. 2. Mengetahui hasil evaluasi tingkat satisfication pada interface ESS menggunakan SUS Method. 3. Mengetahui rekomendasi perbaikan pada interface ESS untuk pengembangan pengujian. Responden sebanyak 136 karyawan divisi Production Control pada PT Toyota Boshoku Indonesia. Sumber data yang digunakan terdiri dari data primer dan data sekunder. Tingkat keberhasilan pengukuran learnability dengan standar deviasi 0,0987, menunjukkan bahwa tingkat keberhasilan 94% dan 100%. Tingkat efisiensi diukur berdasarkan waktu penyelesaian tugas, dengan standar deviasi 2.36. Diperkirakan tingkat efisiensi berada di antara 0.00298 dan 0.01952 detik. Menunjukkan bahwa peserta uji menyelesaikan tugas pada kecepatan rata-rata antara 0,2% hingga 1% setiap detiknya. Pengukuran error rate berdasarkan standar deviasi 2.36 perkiraan nilai error rate berada di antara 0.3872 dan 0,4108. Nilai rata-rata hasil evaluasi kepuasan SUS adalah 99,76 termasuk dalam kategori "Acceptable" dengan penilaian kata sifat “Best Imaginable”.
ANALYSIS OF THE QUALITY OF "ONLINE EQUIVALENT" E-LEARNING USING WEBQUAL 4.0 AND IPA METHODS Rahman, Taufik; Azizah, Alfi
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.3792

Abstract

Abstract: The use of e-learning in non-formal education is increasingly important to support the improvement of access to learning, one of which is through the online platform. This study aims to analyze the quality of online services using WebQual 4.0 and Im-portance Performance Analysis (IPA) methods to evaluate the suitability between user expectations and perceptions. The research method used a quantitative approach by distributing questionnaires to active users, then analyzed using the WebQual Index to measure the overall quality of the system as well as the IPA to determine improvement priorities. The results showed that the quality of SeTARA Online was relatively good with a WebQual Index value of 0.798. However, there is still a gap between user expectations and satisfaction with a negative gap value of -0.238. The IPA analysis identified indicators in Quadrant I as priority improvements, especially in the aspects of service interaction and information presentation. These findings underscore the need for continuous development of features and technical support to optimize the user experience. The conclusion of this study suggests that there should be improvements in priority indicators to increase user satisfaction, as well as strengthen the effectiveness of online learning. Advanced research can expand variables, compare with other platforms, and combine quantitative and qualitative analysis methods for more comprehensive results. Keywords: e-learning; importance performance analysis; quality of service; online equivalent; webqual 4.0
Komparasi Machine Learning Berbasis Pso Untuk Prediksi Tingkat Keberhasilan Belajar Berbasis E-Learning Saputra, Elin Panca; Nurajizah, Siti; Maulidah, Mawadatul; Hidayati, Nadiyah; Rahman, Taufik
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 2: April 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20236469

Abstract

Perkembangan bidang teknologi memiliki aspek perkermbangan yang begitu cepat. penelitian kami memiliki tujuan untuk mentransmisikan sebuah pengetahuan tentang machine learning yang telah menjadi begitu popular digunakan hingga saat ini, pada penelitian ini bagaimana mendapatkan fitur seleksi atribut dan mendapatkan hasil prediksi dari pembelajaran pada Universitas atau lembaga Pendidikan yang menerapkan belajar dengan metode pembelajaran jarak jauh ataupun e-learning di era pandemic ini. Permasalahan pada penelitian ini yaitu jumlah atribut pada data dapat mengurangi akurasi, maka dari percobaan dengan beberapa algoritma pada machine learning kami mencoba menerapkan Particle Swarm Optimizatio(PSO) untuk meningkatkan akurasi yang lebih tinggi. Maka dari itu dapat disimpulkan penerapan menggunakan algoritma Naïve Bayes(NB) berbasis PSO mendapatkan hasil kenerja dengan bobot sebesar 94.40% dan angka AUC sebesar 94.50%, berikutnya Algoritma Support Vectore Machine(SVM) Berbasis PSO dengan hasil kinerja akurasi sebesar 88.20 dan nilai AUC seberar 91.10%, dan Artificial Neural Network(NN) berbasis Particle Swarm Optimizatio(PSO) menghasilkan skor hasil kinerja akurasi dengan bobot 99.20% dan nilai akurasi sebesar 98.50%, maka Artificial Neural Network(NN)  berbasis PSO memiliki keunggulan lebih besar dari pada algoritma naïve bayer berbasis PSO dan Support Vector Machine(SVM) dengan PSO. Sedangkan atribut yang mempunyai pengaruh menentukan dari algoritma tersebut pada tingkat akurasi adalah Practice Questions, Quizzes, Midterm exams, dan Final exams. terbukti dari penelitian-penelitian kami yang sebelumnya maka algoritma neural network berbasis PSO memang memiliki keunggulan yang begitu baik. Karena ANN merupakan metode yang memiliki perhitungan yang membangun beberapa unit pada saat pemrosesan berdasarkan koneksitas yang saling berhubungan, metode ANN dengan akurasi prediksi dapat menjadi sebuah alat yang efisien dan baik untuk penelitian estimasi dan klasifikasi dalam bidang pendidikan. Abstract The development of the field of technology has a very fast development aspect. our research has the aim of transmitting knowledge about machine learning which has become so popularly used until now, in this study how to get attribute selection features and get predictive results from learning at universities or educational institutions that apply learning by distance learning methods or e-learning. -Learning in this pandemic era. The problem in this study is that the number of attributes in the data can reduce accuracy, so from experiments with several yahoos on machine learning, we tried to apply Particle Swarm Optimizatio (PSO) to increase higher accuracy. Then the application key using the PSO-based Naïve Bayes (NB) algorithm can get performance results with a weight of 94.40% and an-AUC number of 94.50%, then the PSO-based Support Vectore Machine (SVM) Algorithm with a performance result of 88.20 and an AUC value of 91.10%, and Artificial Neural Network-(NN) based on Particle Swarm Optimizatio (PSO) produces an accuracy performance score with a weight of 99.20% and an accuracy value of 98.50%. Support Vector Machine (SVM) with PSO. While the attributes that have an influence to determine the algorithm on the level of accuracy are Practice Questions, Quizzes, Mid-Semester Exams, and Final Exams. it is evident from our previous studies that the PSO-based neural network algorithm does have a very good advantage. based on ANN is a method that has calculations that build several units of interconnected connectivity, the ANN method with predictive accuracy can be an efficient and good tool for forecasting and classification research in the field of education.
APPLICATION OF PARTICLE SWARM OPTIMIZATION SUPPORT VECTOR MACHINE FOR ELECTRICAL INSTALLATION CERTIFICATION PREDICTION Priyono, Priyono; Panca Saputra, Elin; Suswandi, Suswandi; Rahman, Taufik
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 2 (2025): Maret 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i2.3418

Abstract

Abstract: Feature selection is a crucial process that is very important to improve the performance of machine learning models, in accordance with data preprocessing. The feature selection process can be considered as a global combinatorial optimization problem in machine learning, which reduces the number of features, eliminates irrelevant data, and produces acceptable classification accuracy. The purpose of this study is to predict or determine the results of the electrical installation operation feasibility test based on data and obtain attribute selection features, and obtain accuracy level results. The Particle Swarm Optimization (PSO) approach is used to select the right characteristics to determine the results of the electrical installation operation feasibility test because attribute selection is needed in data analysis, because the PSO method will increase accuracy than just SVM in determining attribute selection. If SVM is used with PSO, the accuracy value is 96% and AUC is 0.994%, while the SVM method produces an accuracy level of 94.89% and AUC of 0.994%. With this finding, the accuracy value increases by 2%, making it a very good categorization category. It has been proven that the use of Particle Swarm Optimization (PSO) based algorithms can improve and improve results.            Keywords: PSO; SVM; Certification Abstrak: Pemilihan fitur merupakan proses krusial yang sangat penting untuk meningkatkan kinerja model machine learning, sesuai dengan praproses data. Proses pemilihan fitur dapat dianggap sebagai masalah optimasi kombinatorial global dalam pembelajaran mesin, yang mengurangi jumlah fitur, menghilangkan data yang tidak relevan, dan menghasilkan akurasi klasifikasi yang dapat diterima. Tujuan dari penelitian ini adalah untuk memprediksi atau menentukan hasil uji kelayakan operasi instalasi listrik berdasarkan data dan memperoleh fitur pemilihan atribut, serta memperoleh hasil tingkat akurasi. Pendekatan Particle Swarm Optimization (PSO) digunakan untuk memilih karakteristik yang tepat untuk menentukan hasil uji kelayakan operasi instalasi listrik karena pemilihan atribut diperlukan dalam analisis data, karena metode PSO akan meningkatkan akurasi dari pada hanya SVM dalam menentukan pemilihan atribut. Jika SVM digunakan dengan PSO, nilai akurasinya adalah 96% dan AUC sebesar 0,994%, sedangkan metode SVM menghasilkan tingkat akurasi sebesar 94,89% dan AUC sebesar 0,994%. Dengan temuan ini, nilai akurasi meningkat sebesar 2%, menjadikannya kategori kategorisasi yang sangat baik. Telah terbukti bahwa penggunaan algoritma berbasis Particle Swarm Optimization (PSO) dapat meningkatkan dan memperbaiki hasil. Kata kunci: PSO; SVM; Sertifikasi 
Pengembangan Aplikasi Penerimaan Peserta Didik Baru Berbasis Web Menggunakan Metode Agile Rahman, Taufik; Ramdani, Muhammad Farhan; Kuswanto, Herman
Jurnal Ilmiah SINUS Vol 24, No 1 (2026): Vol. 24 No. 1, Januari 2026
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v24i1.1005

Abstract

This study aims to develop a web-based New Student Admissions (PPDB) application that simplifies the registration process, improves administrative efficiency, and ensures the accuracy of prospective student data. The development method used is Agile, which allows for iterative system design with user input at every stage of development. Data was collected through application trials with respondents consisting of school staff and prospective students, and analyzed using quantitative and qualitative approaches to assess ease of use, efficiency, and user satisfaction. The results showed that 90% of respondents considered the application to simplify registration, 85% of staff reported reduced administrative time, and 87% of users were satisfied with the available system. The system successfully collected and verified prospective student data with up to 92% accuracy, and generated registration reports quickly and accurately. These findings confirm that the application of Agile methods in the development of a web-based PPDB application is effective in meeting school administrative needs and improving user experience, while providing a basis for the development of similar systems in other educational institutions.
Sentiment Analysis of Honor of Kings Game Reviews on Google PlayStore Using Naive Bayes and SVM Rahman, Taufik; Saputra, Haikal Fulvian; Kuswanto, Herman
Voteteknika (Vocational Teknik Elektronika dan Informatika) Vol 13, No 4 (2025): Voteteknika (Vocational Teknik Elektronika dan Informatika)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/voteteknika.v13i4.135131

Abstract

This study aims to conduct a sentiment analysis of user reviews of the Honor of Kings game on Google PlayStore using the Naive Bayes (NB) algorithm and Support Vector Machine (SVM) as a machine learning approach. The research gap raised in this study lies in the lack of comparative studies that quantitatively measure the performance of the two classic algorithms on Indonesian-language mobile game review data, as well as the absence of numerical mapping of sentiment distribution that describes user perceptions proportionally. The dataset used consisted of 1000 reviews, which after the manual labeling process was divided into 780 positive reviews (52%), 540 negative reviews (36%), and 180 neutral reviews (12%). The quantitative objective of this study was to measure and compare the levels of accuracy, precision, recall, F1-score, and AUC of the two models to determine the most effective algorithm in classifying user opinions. The test results showed that the SVM model produced an accuracy of 75.3% with an AUC value of 0.82, while the NB model obtained an accuracy of 71.1% with an AUC of 0.78. Based on the confusion matrix, SVM is able to reduce misclassification of negative and neutral sentiments, which are generally difficult to distinguish due to the distribution of sparse text features. Scientifically, this study contributes by showing that SVM is more optimal than NB in handling unbalanced review data, and confirms the importance of feature weighting and AUC validation as indicators of model reliability. Practically, the results of this study can be used by the developers of Honor of Kings to evaluate aspects of the user experience based on the sentiment patterns identified, especially in improving server stability, character balance, and player satisfaction.Keywords— Sentiment Analysis, Naive Bayes, Support Vector Machine, Honor of Kings, Google PlayStore. 
Designing a Cashier Website for Warkop Disini Aja Using the Laravel Framework with UAT Testing and Usability Testing Checklist Rahman, Taufik; Rahmanaufal, Fatta Rahmanaufal
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10392

Abstract

Advances in information technology have a significant influence on various sectors, including Micro, Small, and Medium Enterprises (MSMEs). However, most MSMEs still have not adopted digital systems in their operational activities. One example is Warkop Diini Aja, which until now still applies manual recording to the cashier transaction process. The use of the conventional system poses various obstacles, such as delays in making reports, potential for recording errors, and difficulties in monitoring sales history. Based on these problems, this research aims to design a web-based cashier system that is able to replace manual methods to be more efficient and structured. System development is carried out using the Laravel framework with a waterfall software development model, which includes the stages of needs analysis, design, implementation, testing, and maintenance. Research data was obtained through direct observation of operational processes, interviews with business owners, and literature review to strengthen the theoretical basis. The developed system has two main roles, namely admin and cashier, which are equipped with menu management features, sales transactions, export reports in digital format, and transaction history monitoring. Based on the results of the User Acceptance Testing (UAT) test, all system functions are declared to run according to user needs. In addition, the results of the Usability testing Checklist show a user satisfaction rate of 95%, which is classified as very feasible. Thus, this website-based cashier system has been proven to be able to improve operational efficiency, make it easier to record transactions, strengthen financial report transparency, and support more modern and digital business management.
The Implementation of Vlan Segmentation and Access Control List Security at PT. Justus Kimiaraya Taufik Rahman; Aditia Firdaus Saputra
EDUTIC Vol 13, No 1: 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v13i1.31787

Abstract

In a corporate environment with various divisions and high data exchange activity, efficient and secure network management becomes crucial. PT. Justus Kimiaraya, as a distribution and production company, requires a network system capable of supporting inter-division data communication without compromising performance or security. This research aims to implement Virtual Local Area Network (VLAN) technology to enhance network segmentation and restrict inter-division access according to operational needs. The methods used include literature studies, analysis of existing network requirements, VLAN topology design using Cisco Packet Tracer, implementation of VLAN and Access Control List (ACL), and performance testing before and after implementation. The test results show that VLANs successfully separate data traffic between divisions, preventing interference. Additionally, ACLs implemented on the router effectively limit inter-VLAN access, especially for divisions not authorized to access certain services, such as switchs or other division networks.
Co-Authors Abdul Rofik, Abdul Ade Nurman Saputra Aditia Firdaus Saputra Ahmad Sahroni, Abdul ahmad yani Alfan Khudori Alfi Azizah Ananda Bagus Pramastya Andi Andrean Maulana Putra Arga Putra Rafi Arthur Bayu Segara Bayu Segara Aryo Tunjung Kusumo Bakhtiar Rifai Bambang Wijonarko Buya Ibrahim Chairudin, Muhammad Rio Dedy Kurniawan Eko Sulistianto Ervian Dwi Anggara Eryani, Trisna Waty Riza Fatwa Aulia Rahman Felix Wuryo Handono Giatika Chrisnawati Giovanni Maria Vianney Tobia Mariatmojo Hadi Candra, Tri Hafis Nurdin Herman Kuswanto Herman Kuswanto Herman Kuswanto, Herman Iqbaluz Zain Nibras Irfan Maulana Jeki Saputra Kautsar Imam Hidayat Kusumo, Aryo Tunjung Lingga Wijaya, Harma Oktafia Lukman Hakim Mawadatul Maulidah Moh Faisal Abdullah Muhamad Rabb Arasy Muhammad Farid Anfasa Farid Anfasa muhammad qomaruddin Muhammad Qomaruddin Muhammad Ulin Nuha Nadiyah Hidayati Nur Alfian, Calvin Pamungkas, Evan Tri Panca Saputra, Elin Pramastya, Ananda Bagus Priyono Priyono Qori Aprianto Rahmanaufal, Fatta Rahmanaufal Ramdani, Muhammad Farhan Rifqy Muzhaky Adha Rozen, Ilham Saputra, Haikal Fulvian Saputro, Achmat Yulyadi Satrianansyah, Satrianansyah Siti Nurajizah Sobari, Irwan Agus Sudibyo, Aji Sumarna Sumarna Sumarna Sumarna Sumarna Sumarna Sumarna Surya Gunadi Suswandi, Suswandi Teguh Rahmat Zaini Wardoyo, Rifqi Choiri