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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jurnal Teknisi JITCoS : Journal of Information Technology and Computer System
Muhammad Fathir Aulia
Universitas Islam Negeri Sumatera Utara
Computer Science & IT Control & Systems Engineering

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Klasifikasi Biner Bipolar Pada Data Kusioner Pelamar Asisten Laboratorium Menggunakan Model Hebbian Dan Perceptron Muhammad Fathir Aulia; Armansyah Armansyah
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 8, No 2 (2025): Juli
Publisher : Akademi Ilmu Komputer Ternate

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v8i2.324

Abstract

Abstrak: Penelitian ini bertujuan untuk mengimplementasikan model Hebbian dan Perceptron untuk klasifikasi data kuesioner pelamar Asisten Laboratorium yang dikonversi ke format biner dan bipolar. Data kuesioner mengukur ketertarikan dan pengetahuan pelamar terkait bidang ilmu komputer guna menganalisis efektivitas, kecepatan, dan akurasi masing-masing model. Hasil implementasi menunjukkan bahwa model Hebbian dengan input biner tidak mengenali pola hingga epoch ke-10, sedangkan dengan input bipolar berhasil pada epoch ke-2. Model Perceptron dengan input biner mengenali pola pada epoch ke-2, sementara dengan input bipolar pada epoch ke-3. Kedua model dilatih dengan bobot dan bias awal = 0, serta parameter Perceptron berupa threshold (θ) = 0.5 dan learning rate (η) = 0.1. Dari empat pelamar, dua berminat mendaftar. Data dianalisis menggunakan model Hebbian dan Perceptron untuk mengevaluasi ketepatan, kecepatan, akurasi, serta efektivitas. Hasilnya, model Perceptron lebih direkomendasikan karena fleksibel dan mampu bekerja dengan format biner serta bipolar. Temuan ini memberikan wawasan dalam memilih model klasifikasi yang tepat untuk seleksi pelamar Asisten Laboratorium.Kata kunci: Klasifikasi, Hebbian, Perceptron, Biner - BipolarAbstract: This research aims to implement the Hebbian and Perceptron models for the classification of Laboratory Assistant applicant questionnaire data converted to binary and bipolar formats. The questionnaire data measures the applicant's interest and knowledge related to the field of computer science to analyze the effectiveness, speed, and accuracy of each model. The implementation results show that the Hebbian model with binary input does not recognize patterns until the 10th epoch, while with bipolar input it succeeds at the 2nd epoch. The Perceptron model with binary input recognized the pattern at the 2nd epoch, while with bipolar input at the 3rd epoch. Both models were trained with initial weight and bias = 0, and Perceptron parameters of threshold (θ) = 0.5 and learning rate (η) = 0.1. Of the four applicants, two were interested in applying. The data was analyzed using Hebbian and Perceptron models to evaluate precision, speed, accuracy, and effectiveness. As a result, the Perceptron model is more recommended as it is flexible and able to work with binary as well as bipolar formats. The findings provide insights in choosing the right classification model for Laboratory Assistant applicant selection.Keywords: Classification, Hebbian, Perceptron, Binary - Bipolar
Implementation of Finite State Automata on Pizza Vending Machine System Muhammad Fathir Aulia; Diky Suryandi; Jesron Nainggolan
Journal of Information Technology and Computer System Vol. 1 No. 1 (2025): June
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i1.3

Abstract

This study aims to implement Finite State Automata (FSA) on a pizza machine. FSA is a theoretical computational model used to describe the behavior of a system that can change discretely from one state to another. A pizza machine is a machine used to make pizza automatically. In this study, we design and implement FSA on a pizza machine to regulate the pizza making process. FSA consists of a number of states and transitions between those states. Each state represents a certain stage in the pizza making process, such as adding ingredients, mixing dough, and baking. The programming language and algorithm used are appropriate for implementing FSA on a pizza machine. When the machine is turned on, it will start in the initial state. Then, based on the input given, the machine will switch between different states according to the specified transition rules. By implementing FSA, this study successfully automated the pizza making process on the machine. This reduces dependence on human intervention and increases production efficiency. By using FSA, the pizza machine can operate automatically and produce pizza with high accuracy and efficiency. This study contributes to the development of automation in the food industry and improves the understanding of how to apply FSA in the context of real-world applications. In this study, FSA is used to control a muffin machine, but the FSA concept can also be used in various other automation applications.
Transfer Learning Implementation with MobileNetV2 for Cassava Leaf Disease Detection Muhammad Fathir Aulia; M. Khalil Gibran; Nur Shafwa Aulia Sitorus; Agung Nugroho; Nayla Faiza; Hervilla Amanda R. Siregar
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4442

Abstract

Cassava (Manihot esculenta) is one of Indonesia’s key agricultural commodities but is vulnerable to various leaf diseases, such as Cassava Bacterial Blight (CBB) and Cassava Mosaic Disease (CMD). These diseases often exhibit similar visual symptoms, making it challenging for farmers to accurately identify them through manual observation. This study aims to develop an automatic cassava leaf disease detection system based on transfer learning, utilizing the MobileNetV2 architecture. The dataset used consists of 1,500 images, evenly distributed across three categories: CBB, CMD, and healthy leaves. The data underwent preprocessing, augmentation, and model training, including fine-tuning of the last 20 layers of the MobileNetV2 model. Evaluation results indicated that the model achieved an accuracy of 67% on the test set, with the highest performance in detecting Cassava Mosaic Disease, reflected by an F1-score of 0.75. These results demonstrate the potential of MobileNetV2 as a lightweight and efficient solution for detecting cassava leaf diseases, particularly when supported by a larger and more diverse dataset. This research serves as a foundation for developing mobile-based diagnostic tools to help farmers make faster and more accurate decisions in the field.
APPLICATION OF WEIGHTED AVERAGE ALGORITHM IN RECREATIONAL PARK TOURIST DESTINATION RECOMMENDATION SYSTEM BASED ON GOOGLE MAPS USER RATINGS Nayla Faiza; Hervilla Amanda R. Siregar; Nur Shafwa Aulia Sitorus; Agung Nugroho; Muhammad Fathir Aulia; Mhd Furqan
JURNAL TEKNISI Vol. 5 No. 2 (2025): Agustus 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/teknisi.v5i2.3790

Abstract

Abstract: The development of digital technology has changed the behavior patterns of tourists in choosing travel destinations. Google Maps is now not only used to find restaurant locations but has also become the main source for searching nearby tourist destinations based on user ratings and reviews. This research aims to build a recommendation system for recreational park tourist destinations in Medan City by applying the Weighted Average algorithm using Google Maps user rating data. The data used comes from reviews by five users of five popular recreational parks in Medan City during the period from January 1, 2025, to April 30, 2025. The Weighted Average algorithm was chosen because it can provide a more objective and fair assessment by taking into account the weight of each rating given by users. As a result, this system can recommend the best recreational parks based on user experiences related to cleanliness, parking facilities, toilets, security, running paths, and accessibility. It is hoped that this system can help tourists choose destinations that meet their needs and preferences, as well as provide a more enjoyable and satisfying travel experience.Keywords : digital technology; google maps; recommendation system; weighted average algorithmAbstrak: Perkembangan teknologi digital telah mengubah pola perilaku wisatawan dalam memilih destinasi wisata. Google Maps kini tidak hanya digunakan untuk mencari lokasi restoran, tetapi juga menjadi sumber utama dalam mencari destinasi wisata terdekat berdasarkan rating dan ulasan pengguna. Penelitian ini bertujuan untuk membangun sistem rekomendasi destinasi wisata taman rekreasi di Kota Medan dengan menerapkan algoritma Weighted Average menggunakan data rating pengguna Google Maps. Data yang digunakan berasal dari lima ulasan pengguna terhadap lima taman rekreasi populer di Kota Medan selama periode 1 Januari 2025 hingga 30 April 2025. Algoritma Weighted Average dipilih karena mampu memberikan penilaian yang lebih objektif dan adil dengan memperhatikan bobot setiap rating yang diberikan pengguna. Hasilnya, sistem ini dapat merekomendasikan taman rekreasi terbaik berdasarkan pengalaman pengguna terkait aspek kebersihan, fasilitas parkir, toilet, keamanan, lintasan lari, dan aksesibilitas. Diharapkan sistem ini dapat membantu wisatawan dalam memilih destinasi yang sesuai dengan kebutuhan, preferensi, dan memberikan pengalaman wisata yang lebih menyenangkan dan memuaskan. Kata Kunci: google maps; sistem rekomendasi; teknologi digital; weighted average algorithm
Co-Authors Agung Nugroho Armansyah Armansyah Diky Suryandi Hervilla Amanda R. Siregar Jesron Nainggolan M. Khalil Gibran Mhd Furqan Nayla Faiza Nur Shafwa Aulia Sitorus