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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Pendidikan Islam Iqra' IKRA-ITH EKONOMIKA Jurnal Kreativitas PKM Journal of Information Systems and Informatics Jurnal Teknik Informatika (JUTIF) Journal La Multiapp Indonesian Journal of Innovation Studies Jurnal Minfo Polgan (JMP) Jurnal Locus Penelitian dan Pengabdian Jurnal Sains Informatika Terapan (JSIT) Innovative: Journal Of Social Science Research
Hasibuan, Alfiansyah
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Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Implementasi Algoritma Rule-Based Classification pada Aplikasi Arsip Web Menggunakan Metode RAD dan Evaluasi ISO/IEC 25010 Runtu, Rafael Revi; Maramis, Glenn D. P.; Hasibuan, Alfiansyah
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15505

Abstract

Pengelolaan arsip surat menyurat memiliki peran krusial dalam menunjang efektivitas administrasi pada suatu instansi. Penelitian ini mengusulkan perancangan aplikasi arsip berbasis web yang mampu melakukan klasifikasi surat secara otomatis berdasarkan isi dokumen. Mekanisme klasifikasi didasarkan pada algoritma Rule-Based Classification dengan memanfaatkan kata kunci tertentu untuk menentukan kategori surat, seperti undangan, permohonan, laporan, maupun surat tugas. Proses pengembangan sistem menerapkan metode Rapid Application Development (RAD) yang menekankan iterasi cepat serta keterlibatan aktif pengguna. Kualitas perangkat lunak diuji menggunakan model ISO/IEC 25010 dengan fokus pada tiga aspek utama, yaitu functional suitability (kesesuaian fungsional), usability (kemudahan penggunaan), serta reliability (keandalan). Hasil evaluasi menunjukkan aplikasi memperoleh skor 95% pada kesesuaian fungsional, 90% pada kemudahan penggunaan, dan 85% pada aspek keandalan. Temuan ini menegaskan bahwa sistem yang dikembangkan layak digunakan dan berpotensi meningkatkan efisiensi dalam pengelolaan arsip surat secara digital.
Implementasi Algoritma First Come First Served (FCFS) Pada Sistem Informasi Pengelolaan Antrian Online Di UPC Pegadaian Tomohon Kumowal, Jaklin Agnes; Kumajas, Sondy Campvid; Hasibuan, Alfiansyah
IKRAITH-EKONOMIKA Vol. 9 No. 1 (2026): IKRAITH-EKONOMIKA Vol 9 No 1 Maret 2026
Publisher : Universitas Persada Indonesia YAI

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Abstract

Pegadaian merupakan lembaga keuangan yang memberikan layanan gadai kepada masyarakat. Salah satu permasalahan yang masih dihadapi di UPC Pegadaian Tomohon adalah sistem antrian yang masih dilakukan secara manual, sehingga menyebabkan ketidakteraturan pemanggilan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan algoritma FCFS pada sistem informasi pengelolaan antrian online berbasis web di UPC Pegadaian Tomohon. Metode pengembangan sistem adalah Extreme Programming. Sistem dikembangkan menggunakan PHP dan MySQL, serta diintegrasikan dengan WhatsApp API untuk pengiriman notifikasi dan API harga emas untuk menampilkan informasi harga emas secara real-time. Hasil penelitian menunjukkan bahwa sistem antrian berbasis web dengan algoritma FCFS mampu mengelola antrian secara adil berdasarkan urutan kedatangan nasabah. Integrasi notifikasi WhatsApp membantu nasabah memperoleh informasi giliran secara langsung, sementara fitur harga emas real-time memberikan nilai tambah informasi layanan serta meningkatkan kualitas layanan.
A Digital Image Processing–Based Moler Disease Detection System for Shallot Leaves Wahyuni, Reski; Hasibuan, Alfiansyah; Santa, Kristofel
Journal La Multiapp Vol. 7 No. 1 (2026): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v7i1.2738

Abstract

This study aims to design and develop a leaf moler disease detection system on shallots (Allium cepa L.) based on digital image processing in Enrekang Regency, South Sulawesi. Moler disease caused by the fungus Fusarium oxysporum f. sp. cepae is one of the main factors that reduce the quality and productivity of shallots. So far, disease identification is still done manually through direct observation by farmers, which is subjective and time-consuming. To overcome this problem, this study applies the Convolutional Neural Network (CNN) algorithm to automatically classify shallot leaf images into two categories, namely healthy and infected with moler disease. The number of datasets used is 502 images, consisting of 251 healthy images and 251 infected images, with data division of 70% for training, 15% for validation, and 15% for testing. The CNN architecture used consists of convolution, pooling, flatten, and fully connected layers with ReLU and sigmoid activation functions in the output layer. The training process used the Adam optimizer with a learning rate of 0.001 and a binary cross-entropy loss function. Test results showed a training accuracy of 97.14%, a validation accuracy of 94.73%, and a testing accuracy of 97.37%, indicating the model has a good level of precision and generalization ability without overfitting. This system is implemented as a Flask-based web application that allows users to upload leaf images and obtain detection results instantly. This system is expected to help farmers detect diseases more quickly and increase shallot productivity in Enrekang Regency.
Leaf Type Recognition System Using Image Processing Method Using Convolutional Neural Network Algorithm Kolauw, Evan; Hasibuan, Alfiansyah; Kumajas, Sondy C
Journal La Multiapp Vol. 7 No. 2 (2026): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v7i2.3057

Abstract

A digital image-based leaf recognition system is one of the modern solutions in the fields of botany and agriculture to identify plants automatically. This study developed a leaf recognition system using image processing methods and Convolutional Neural Network (CNN) algorithms. CNN was chosen because of its ability to independently extract features through convolution layers, thus capturing important visual patterns such as shape, edges, textures, and leaf veins without requiring manual feature engineering processes. The research dataset consists of a collection of leaf images from several types of plants obtained through direct photo-taking and public dataset sources. Each image goes through a pre-processing stage, including cropping, resizing, image quality enhancement, and pixel normalization to ensure data consistency before entering the training stage. The CNN model is designed with several convolutional layers, pooling, activation functions, and fully connected layers to produce optimal classification performance. Model training is carried out by dividing training and testing data, as well as augmentation techniques to increase image variation. System performance is evaluated using accuracy, precision, recall, and confusion matrix. The test results show that the CNN model is able to recognize leaf types with a high level of accuracy and is stable under various test conditions, including variations in lighting and shooting angles. Overall, this study proves that CNN is an effective and reliable approach in building an automatic leaf recognition system. This system has the potential to be applied in the fields of precision agriculture, mobile application-based plant identification, and botanical research that require speed and accuracy in plant classification.
The Implementasi Algoritma Kriptografi AES-128 (RIJNDAEL) Untuk Keamanan Informasi Dokumen Rahasia PT. PLN (PERSERO) UP2B Sistem Minahasa Liow, Merlin Aprilia; Rorimpandey, Gladly Caren; Hasibuan, Alfiansyah
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

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Abstract

The rapid advancement of digital technologies in the electricity sector has increased the demand for stronger information security systems, particularly in managing confidential documents that support operational decision-making. PT PLN (Persero) UP2B Sistem Minahasa serves as a critical unit responsible for regulating and monitoring electrical load distribution in North Sulawesi, making the protection of internal documents essential to maintaining operational integrity and confidentiality. However, the current document management process still relies on conventional methods and lacks the support of modern encryption technologies, leaving sensitive data vulnerable to unauthorized access, information leakage, and misuse. These risks became more evident following several data breach incidents reported in 2022 and 2023. This research focuses on developing a digital document security system utilizing the Advanced Encryption Standard (AES-128) algorithm to protect files in PDF, DOCX, XLS, and TXT formats. The research stages include requirement analysis, system design, implementation using PHP, MySQL, and OpenSSL, and performance testing for the encryption and decryption processes. The results indicate that AES-128 efficiently encrypts documents and produces files that cannot be accessed without a valid key, while the decryption process successfully restores the original content with an accuracy level of up to 90%. Overall, the implementation of this system significantly improves document security within UP2B Sistem Minahasa and can serve as a reference for strengthening information security policies across PLN units.
Implementation of a Web-Based Media Partnership Registration Information System Using Waterfall Model Baridji, David Vernando; Hasibuan, Alfiansyah; Tinambunan, Medi H.
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1411

Abstract

The Minahasa Regency Department of Communication and Informatics manages public information and cooperation with press media organizations, yet press media partnership registration is still handled manually. This causes processing delays, data duplication, limited traceability, and poor visibility of verification status, increasing administrative workload and reducing service transparency for applicants. This study implements a web-based Press Media Partnership Registration Information System to digitalize and standardize the registration workflow. Development follows the Waterfall model, including requirement analysis, system design, implementation, testing, and refinement to ensure structured deliverables suitable for government environments. The system provides CRUD-based data management, administrator-led verification with status tracking, and automated email notifications for verification outcomes. Functional validation uses Black Box Testing to evaluate input-output behavior against predefined specifications. Test results show that core modules—account registration, login, partnership submission, verification (approve/reject with notes), CRUD operations, session control, and email notification—operate correctly and meet functional requirements. The implemented system is feasible for operational use and improves efficiency, data accuracy, traceability, and transparency in local government press media partnership administration.
Analisis Efektivitas Sistem Rekomendasi Berbasis Random Forest untuk Edukasi Rehabilitasi Narkoba di Masyarakat Kota Manado Silaarta, Hezeki Farell; Hasibuan, Alfiansyah; Kumajas, Sondy C.
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 4 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i4.5701

Abstract

Penyalahgunaan narkoba di Kota Manado menunjukkan tren peningkatan yang signifikan dan menjadi tantangan serius bagi upaya pencegahan serta edukasi rehabilitasi. Penelitian ini bertujuan menganalisis efektivitas sistem rekomendasi materi edukasi rehabilitasi berbasis algoritma Random Forest, yang dirancang untuk menyajikan konten pembelajaran terpersonalisasi sesuai karakteristik pengguna. Metode yang digunakan adalah Research and Development (R&D) dengan desain quasi-experimental melalui pretest-posttest control group design. Sampel penelitian terdiri atas 181 responden, terbagi dalam kelompok eksperimen (91 orang) dan kontrol (90 orang). Sistem dikembangkan menggunakan Python, Flask, dan PostgreSQL, dengan Random Forest sebagai algoritma klasifikasi berdasarkan variabel usia, jenis kelamin, minat edukasi, pekerjaan, dan skor pre-test. Hasil pengujian menunjukkan bahwa model Random Forest memiliki kinerja klasifikasi yang sangat tinggi, dengan akurasi, precision, recall, dan F1-score masing-masing mencapai 100%. Namun, uji efektivitas sistem memperlihatkan tidak adanya perbedaan signifikan antara kelompok eksperimen dan kontrol, baik pada nilai post-test (p = 0,3023) maupun gain score (p = 0,7503). Meskipun demikian, secara deskriptif kelompok eksperimen menunjukkan kecenderungan peningkatan skor lebih tinggi dibandingkan kelompok kontrol. Temuan ini mengindikasikan bahwa meskipun sistem rekomendasi bekerja optimal secara teknis, dampak intervensi terhadap peningkatan pemahaman belum signifikan secara statistik. Penelitian ini berkontribusi pada pengembangan sistem edukasi digital berbasis machine learning di bidang rehabilitasi narkoba, serta membuka peluang perbaikan melalui penambahan variabel prediktor, pengayaan level materi, dan perbandingan dengan algoritma lain pada penelitian selanjutnya.
Co-Authors Baridji, David Vernando Derek, Sesilia Djami Olii Gladly Caren Rorimpandey Keith Francis Ratumbuisang Kolauw, Evan Kristofel Santa Kumajas, Sondy C Kumajas, Sondy C. Kumajas, Sondy Campvid Kumowal, Jaklin Agnes Liow, Merlin Aprilia Mantik, Felitia Theona Geofani Maramis, Glenn D. P. Marpaung, Effenril Agung Merriam Listiany Modeong Mokoginta, Fadlia Mustafa Musung, Matthew Albert Alexander Putra, Purwa Hasan Quido Conferti Kainde Raharusun, Oliver Simon Hardianto Runtu, Rafael Revi Silaarta, Hezeki Farell Situmorang, Boby Putra Sondy C. Kumajas Tinambunan, Medi H. Wahyuni, Reski Walujan, Bryan Chelvan Winarto, Lewi Yusrel