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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Sinkron : Jurnal dan Penelitian Teknik Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Riset Informatika JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Mantik JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Scientia Computer Science (CO-SCIENCE) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Innovative: Journal Of Social Science Research Jurnal Multidisiplin Sahombu
Desmulyati, Desmulyati
Universitas Bina Sarana Informatika
Aerospace Engineering Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Public Health Transportation Other

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Classification of Payment Patterns for Toyota Car Sales Using the Decision Tree Algorithm Siregar, Martua Hami; Saiyar, Hafdiarsya; Desmulyati, Desmulyati; Noviansyah, Mohammad
Jurnal Multidisiplin Sahombu Vol. 5 No. 04 (2025): Jurnal Multidisiplin Sahombu, May - Juny (2025)
Publisher : Sean Institute

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

Abstract

Car sales represent a business sector highly dependent on the implementation of appropriate payment strategies to enhance customer satisfaction and operational efficiency. This study aims to classify payment patterns in Toyota car sales using the Decision Tree algorithm. Historical sales data were utilized to identify various attributes influencing payment methods, such as cash, credit, or leasing.Through processes of preprocessing, feature selection, and model training, the Decision Tree algorithm successfully established clear classification patterns based on variables such as payment type, gender, car type, and car category. The research findings indicate that the Decision Tree method not only provides a high level of accuracy in classifying payment patterns but also produces models that are easily interpretable by business decision-makers. Thus, the implementation of this classification technique is expected to assist companies in designing more effective and targeted sales and promotional strategies.
Identification E-SIM for Motorcycle Security Using Atmega 8 Microcontroller Saiyar, Hafdiarsya; Desmulyati, Desmulyati
Jurnal Riset Informatika Vol. 5 No. 1 (2022): December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i1.182

Abstract

Motorcycle theft is getting more disturbing, so it encourages the author to make security devices on motorbikes. This study has created a security system that can secure motorcycles using an e-SIM based on the Atmega-8 Microcontroller. Where the e-SIM has a chip, the chip itself has 7 bytes. In this case, the authors take advantage of the 7-byte chip in the e-SIM. The e-SIM will replace the motorcycle ignition key. Not only that, but the e-SIM will also give orders to the motor starter to start the motorcycle. Thus, only the owner of the e-SIM who already has a sim can give the motorcycle ON and OFF orders. The research method used is direct observation of the selected object, namely the author's home environment, and conducting literature studies related to the Atmega-8 microcontroller. This study aims to create a security system for motorcycle vehicles to avoid theft and the use of motorcycles for children without driving licenses.
Sistem Pengendali Pintu Garasi Mobil Menggunakan Sensor Reed Switch dan RFID Berbasis Mikrokontroler ATMega Saiyar, Hafdiarsya; Noviansyah, Mohammad; Desmulyati, Desmulyati; Siregar, Martua Hami
Innovative: Journal Of Social Science Research Vol. 4 No. 2 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i2.10345

Abstract

Dalam kehidupan, banyak hal dilakukan di dalam dan di luar ruangan, bahkan kegiatan tersebut tidak terlepas dari keberadaan pintu dimana kita harus membuka atau menutup pintu yang membuat kita merasa enggan melakukannya, secara berulang masuk dan keluar pintu dengan menarik atau mendorong pintu. Melihat kondisi bahwa sebagian besar proses operasi pintu garasi mobil masih dilakukan secara manual di mana intervensi manusia masih terlibat secara langsung, maka akan lebih praktis dan efisien jika pintu garasi dapat membuka sendiri. Oleh karena itu, semakin kompleks proses yang harus diatasi, semakin penting penggunaan sistem minimum ATMega16 untuk memfasilitasi proses tersebut, oleh karena itu penulis terinspirasi untuk membuat Merancang dan Membangun Pengendalian Pintu Mobil Menggunakan Sensor Reed Switch dan Mikrokontroler Berbasis RFID ATMega16. Alat ini berfungsi untuk secara otomatis membuka dan menutup pagar dengan input sensor yang terperinci. Jika kendaraan memiliki pengirim, secara otomatis sistem minimum akan menerima gangguan dari sensor dan akan memberi perintah atau melanjutkan membuka pagar secara otomatis.
Penerapan Algoritma Machine Learning Untuk Deteksi Akses Tidak Sah Pada SIAKAD IAI Al-Ghurabaa Wahyudi Wahyudi; Mohammad Noviansyah; Hafdiarsya Saiyar; Martua Hami Siregar; Desmulyati Desmulyati
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 6 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i6.10021

Abstract

Abstrak: Sistem Informasi Akademik (SIAKAD) merupakan komponen vital dalam pengelolaan data akademik di perguruan tinggi, termasuk Institut Agama Islam Al-Ghurabaa. Akses tidak sah terhadap sistem ini dapat menyebabkan kebocoran data, perubahan nilai, dan gangguan integritas informasi akademik. Penelitian ini bertujuan untuk mengembangkan model deteksi dini terhadap aktivitas akses tidak sah menggunakan algoritma machine learning.Metode penelitian meliputi pengumpulan dan pra-pemrosesan data log akses SIAKAD, ekstraksi fitur perilaku pengguna (frekuensi login, waktu akses, IP address, dan pola aktivitas), serta pelatihan model klasifikasi menggunakan algoritma Random Forest dan Support Vector Machine (SVM). Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score.Hasil pengujian menunjukkan bahwa algoritma Random Forest menghasilkan tingkat akurasi tertinggi sebesar 97,3%, dengan kemampuan deteksi anomali akses yang lebih baik dibanding SVM (93,8%). Model yang diusulkan mampu mendeteksi aktivitas login mencurigakan secara real-time, sehingga dapat menjadi lapisan keamanan tambahan untuk SIAKAD IAI Al-Ghurabaa. Penerapan machine learning dalam keamanan aplikasi akademik terbukti efektif dalam meningkatkan ketahanan sistem terhadap serangan berbasis autentikasi dan penyalahgunaan akun penggunaKata kunci: SIAKAD; keamanan data; deteksi anomali; machine learning; Random Forest; SVM; Abstract: The Academic Information System (SIAKAD) is a vital component of academic data management in higher education institutions, including Institut Agama Islam Al-Ghurabaa. Unauthorized access to this system can lead to data breaches, grade manipulation, and loss of information integrity. This research aims to develop an early detection model for unauthorized access using machine learning algorithms. The methodology includes collecting and preprocessing SIAKAD access log data, extracting behavioral features (login frequency, access time, IP address, and activity patterns), and training classification models using Random Forest and Support Vector Machine (SVM) algorithms. Evaluation metrics used are accuracy, precision, recall, and F1-score. Experimental results show that the Random Forest algorithm achieved the highest accuracy of 97.3%, outperforming SVM (93.8%) in detecting anomalous access attempts. The proposed model can identify suspicious login activities in real-time, providing an additional security layer for SIAKAD IAI Al-Ghurabaa. The study demonstrates that machine learning-based intrusion detection is effective in enhancing system resilience against authentication-based attacks and user account misuse.Keywords: SIAKAD; data security; anomaly detection; machine learning; Random Forest; SVM;
Co-Authors Agus Irawan Andi Taufik Budiman, Ade Surya Chaerullah Fadli Fahrizal Fahrizal Febriyanto Febriyanto Fitria Purwaningsih Hafdiarsya Saiyar Hafdiarsya Saiyar Hafdiarsya Saiyar, Hafdiarsya Imam Budiawan Imam Sujarwo Martua Hami Siregar Mohammad Noviansyah Muhammad Ihsan Nico Hadipratama Perdana Putra, Muhammad Rizki Ramadhina Fajriani, Rieka Rizky Apriansyah Hadi Rizky Fauzi Syahrul Suci Romadhon Try Budi Septiandoko Wahyudi Wahyudi