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All Journal Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat CogITo Smart Journal IT JOURNAL RESEARCH AND DEVELOPMENT Journal of Applied Engineering and Technological Science (JAETS) International Journal of Engineering, Technology and Natural Sciences (IJETS) INOVTEK Polbeng - Seri Informatika
Syafitri, Nesi
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering

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Pemanfaatan Sistem Informasi Pengelolaan Pembayaran Uang Sekolah untuk Peningkatan Layanan Pendidikan di SMK Migas Teknologi Riau Syafitri, Nesi; Suryani, Des; Fadhilla, Mutia; Baskara, Agus
Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Vol 7, No 1 (2024): Februari 2024
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jabdimas.v7i1.14878

Abstract

Sekolah merupakan lembaga pendidikan yang sifatnya formal ataupun informal yang dikelola oleh negara atau swasta dengan tujuan untuk memberikan pengajaran, mengelola, dan mendidik para siswa melalui bimbingan yang diberikan oleh para pendidik. Selain kegiatan pengajaran yang dilakukan di sekolah, terdapat aktivitas lain yang juga dilakukan yaitu pengelolaan sekolah. Salah satu kegiatan pengelolaan yang dilakukan adalah administrasi pembayaran uang sekolah siswa. Pengelolaan sekolah yang baik akan membantu meningkatkan layanan pendidikan yang diberikan kepada siswa, orang tua/wali murid dan stakeholder lainnya. Pada SMK Migas Teknologi Riau Pekanbaru, layanan administrasi pembayaran uang sekolah siswa dirasakan masih kurang optimal. Pelayanan pembayaran uang sekolah yang dilakukan selama ini di sekolah adalah dengan pencatatan pada buku kemudian bendahara akan mengeluarkan kwitansi sebagai bukti pembayaran. Pengelolaan data pembayaran uang sekolah yang dilakukan secara manual menyebabkan munculnya masalah mulai dari pencatatan transaksi, penyusunan laporan keuangan yang sering terlambat, bukti fisik penerimaan yang bisa rusak atau hilang, penumpukan keterlambatan pembayaran siswa karena tidak bisa dimonitor setiap saat dan orang tua/wali murid juga tidak bisa mendapatkan pemberitahuan riwayat pembayaran uang sekolah. Tujuan dari kegiatan pengabdian kepada masyarakat ini adalah membantu sekolah dalam penerapan teknologi pada pengelolaan pembayaran uang sekolah siswa sehingga layanan pendidikan yang baik dapat tercapai. Penerapan teknologi dapat membantu mengurangi kemungkinan beban kerja pegawai, meningkatkan manajemen waktu, dan menghasilkan informasi berkualitas dan up to date. Luaran dari pengabdian ini adalah menghasilkan sebuah sistem informasi pembayaran uang sekolah yang memberikan manfaat pada peningkatan waktu layanan, penghematan waktu kerja, kemudahan akses informasi dan keakuratan informasi yang tersedia.
Prototype of Lighting Intensity Administration in Work Room With Sound Control and Fuzzy Logic Control Syafitri, Nesi; Ibnu Faderi, Raja; Suryani, Des; Labellapansa, Ause
IT Journal Research and Development Vol. 7 No. 1 (2022)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2022.10278

Abstract

The use of indoor and outdoor lighting systems that are still passive makes the use of electrical energy less efficient and classified as wasteful. From the problems that occur in the system that is running, the researchers need to develop a more dynamic system by utilizing the Tsukamoto FLC method and Arduino using voice control to adjust the intensity of the light in the room. After implementing as well as testing the system that has been made, namely the Prototype of Light Intensity Regulator in the Work Room With Voice Control and Fuzzy Logic Control using NodeMCU ESP8266, it is concluded that each component can function according to its function which can be controlled and monitored from the application, implements fuzzy logic control on nodemcu with time and activity input variables obtained from the android application while the room light intensity variable is obtained from the light sensor or LDR. The results of the fuzzy process will adjust the light which is controlled by nodemcu. With the fuzzy logic control in this system, it can adjust the light to the room conditions, making it much more efficient.
Machine Learning-Based Counseling to Predict Psychological Readiness for Aspiring Entrepreneurs Syafitri, Nesi; Farradinna, Syarifah; Arta, Yudhi; Herawati, Icha; Jayanti, Wella
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.553.510-521

Abstract

Machine learning has become an exciting topic in psychology-related research, one of which is counseling psychological readiness for entrepreneurship. An intelligent application developed using a machine learning model to assist the counseling process in measuring a person's psychological readiness for entrepreneurship. This application was generated using the Entrepreneurship Psychological Readiness (EPR) instrument. In this study, to get the most suitable machine learning model, a comparison of 2 (two) machine learning models, namely, Naïve Bayesian (NB) and k-Nearest Neighbor (k-NN), involving 1095 training data. There are 4 (four) prediction classes recommended from the results of counseling: categories not ready for entrepreneurship, given training, guided, and prepared for entrepreneurship. The EPR instrument consists of 33 question items to measure 8 (eight) parameters used as inputs for the prediction process. The data has been randomized, and the experiment has been repeated 5 (five) times to check the consistency of performance of all techniques. 80% of the data was used as training data, and the other 20% was used as testing data. The results of the five (5) trials show that the Naïve Bayesian model provides the most consistent results in predicting a person's psychological readiness for entrepreneurship, with 89.58% accuracy, in testing. Therefore, the Naïve Bayesian model is recommended to be used in psychological counseling to predict a person's readiness for entrepreneurship
Application of Machine Learning for Classifying and Identifying Security Threats Using a Supervised Learning Algorithm Approach Arta, Yudhi; Mohamad Samuri, Suzani; Syafitri, Nesi; Hanafiah, Anggi; Oktaria, Wina; Maripati, Maripati; Pandu Cynthia, Eka
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/aqjdbj22

Abstract

The rapid growth of harmful web content has intensified the demand for intelligent systems capable of accurately classifying cyber threats based on URL patterns. This study evaluates two widely used supervised learning algorithms, Random Forest and Naïve Bayes, for probabilistic classification of multi-class URL datasets. A synthetic dataset comprising 547,775 URLs was designed to reflect realistic threat distribution: benign (65.74%), phishing (14.46%), defacement (14.81%), and malware (4.99%). Each sample included simple structural features such as URL length, number of dots, HTTPS usage, and keyword indicators. Both models were tested using identical stratified train-test splits with varying sample sizes, including focused experiments on 15,000 and 100,000 entries. Results revealed that both models achieved high recall and precision only for the benign class, while failing to detect minority classes. For Random Forest, precision and recall for benign URLs reached 1.00 but dropped to 0.00 for phishing, defacement, and malware in all test scenarios. Naïve Bayes exhibited similar shortcomings, highlighting the impact of class imbalance and limited feature expressiveness. This research concludes that while Random Forest and Naïve Bayes are computationally efficient, they are inadequate for detecting cyber threats without preprocessing techniques such as SMOTE, cost-sensitive learning, or feature enrichment. Future work will explore adaptive hybrid models with contextual features and deep learning frameworks to enhance multi-class detection in real-world cybersecurity scenarios.
Enhancing Intrusion Detection System Performance Using Reinforcement Learning : A Fairness-Aware Comparative Study on NSL-KDD and CICIDS2017 Arta, Yudhi; Samuri, Suzani Mohamad; Syafitri, Nesi
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

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

Abstract

Conventional Intrusion Detection Systems (IDS) often fail to generalize in dynamic network environments, facing challenges with evolving attack patterns and class imbalance. This study aims to evaluate and compare the effectiveness of three Reinforcement Learning (RL) paradigms to enhance IDS adaptability and accuracy against these challenges. This research employs a comparative experimental design, implementing Q-Learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO). These algorithms were systematically evaluated using the NSL-KDD and CICIDS2017 benchmark datasets to represent both legacy and modern network traffic. A fairness-aware evaluation framework was applied, prioritizing the Matthews Correlation Coefficient (MCC) as a primary metric alongside accuracy to ensure robust performance assessment against skewed class distributions. Experimental results demonstrate that PPO significantly outperforms value-based algorithms such as Q-Learning and DQN. On the high-dimensional CICIDS2017 dataset, PPO achieved the highest detection accuracy (96.3%) and MCC (0.913). Confusion matrix analyses confirmed PPO’s capability to simultaneously minimize false positives and false negatives. Conversely, Q-Learning exhibited poor generalization on complex data, while DQN showed improved performance due to deep value approximation but remained less stable than PPO. These findings imply that policy-gradient methods like PPO are superior for real-world IDS deployments where scalability, adaptability, and low error rates are critical. Theoretically, the results suggest that stochastic policy optimization handles complex, continuous state spaces more effectively than traditional value-estimation approaches. This study contributes a rigorous head-to-head comparative analysis of RL algorithms across multiple standard datasets using fairness-aware metrics. It bridges the research gap found in previous studies that often evaluated algorithms in isolation or relied on accuracy metrics that can be misleading in imbalanced security contexts.
Application of Machine Learning for Classifying and Identifying Security Threats Using a Supervised Learning Algorithm Approach Arta, Yudhi; Samuri, Suzani Mohamad; Syafitri, Nesi; Hanafiah, Anggi; Oktaria, Wina; Maripati, Maripati
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 7 No 2 (2025): International Journal of Engineering, Technology and Natural Sciences
Publisher : Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/ijets.v7i2.548

Abstract

The exponential growth of malicious web content has created an urgent demand for intelligent systems capable of accurately classifying cyber threats based on URL patterns. This study investigates the effectiveness of two widely used supervised learning algorithms, Random Forest and Naïve Bayes, in probabilistic classification tasks involving multiclass URL data. A synthetic dataset simulating 547,775 URLs was constructed to reflect realistic threat distribution: benign (65.74%), phishing (14.46%), defacement (14.81%), and malware (4.99%). Each instance was characterized by basic structural features such as length, dot count, HTTPS presence, and keyword indicators. To ensure fairness, both models were evaluated using identical stratified train-test splits across varying sample sizes, including a focused experiment on 15,000 and 100,000 entries. Results consistently revealed that both models exhibited high recall and precision only for the benign class, while failing entirely to detect minority classes. For Random Forest, precision and recall values reached 1.00 for benign URLs, yet dropped to 0.00 for phishing, defacement, and malware across all test sets. Naïve Bayes showed similar performance degradation, highlighting the severe impact of class imbalance and limited feature expressiveness. These findings emphasize the inadequacy of conventional classifiers in highly skewed, security-sensitive environments without preprocessing interventions. The study concludes that while Random Forest and Naïve Bayes offer computational simplicity, their default behavior is biased toward majority classes, rendering them unsuitable for detecting cyber threats without employing resampling techniques (e.g., SMOTE), cost-sensitive learning, or feature augmentation strategies. Future work will explore adaptive hybrid models with contextual features and deep learning frameworks to improve multiclass detection in real-world cybersecurity applications.
Co-Authors Ause Labellapansa, Ause Baskara, Agus Des Suryani Eka Pandu Cynthia Fadhilla, Mutia Farradinna, Syarifah Hanafiah, Anggi Herawati, Icha Ibnu Faderi, Raja Jayanti, Wella Maripati, Maripati mohamad samuri, suzani Oktaria, Wina Samuri, Suzani Mohamad Yudhi Arta, Yudhi