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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Info Sains : Informatika dan Sains Jurnal Ilmiah Teknik Informatika dan Komunikasi Journal of Informatics, Electrical and Electronics Engineering Jurnal Sains Student Research Jurnal Media Akademik (JMA) Jurnal Intelek Dan Cendikiawan Nusantara Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Saturnus: Jurnal Teknologi dan Sistem Informasi
Lailan Sofinah Harahap
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TINJAUAN METODE PENGOLAHAN CITRA DIGITAL UNTUK DETEKSI OBJEK OTOMATIS Nasution, Mansalwa Utama; Lailan Sofinah Harahap; Fajar Syakbani
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.7135

Abstract

Deteksi objek otomatis merupakan bagian penting dalam pengolahan citra digital yang banyak diaplikasikan dalam bidang keamanan, medis, hingga kendaraan otonom. Penelitian ini bertujuan untuk meninjau dan membandingkan beberapa metode deteksi objek berbasis pengolahan citra digital dengan pendekatan klasik dan deep learning menggunakan Python. Metode klasik yang digunakan adalah Canny Edge Detection dan Template Matching, sedangkan pendekatan modern mencakup YOLOv5. Hasil eksperimen menunjukkan bahwa metode berbasis deep learning memberikan akurasi dan kecepatan deteksi yang lebih baik dibandingkan metode klasik. Evaluasi dilakukan berdasarkan metrik presisi, recall, dan Intersection over Union (IoU).
Analisis Perbandingan Filter Median dan Gaussian dalam Mengurangi Noise pada Citra Digital Puji Sri Alhirani; Lailan Sofinah Harahap; Rani Chantika
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 2 (2025): Juli: Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i2.1148

Abstract

. Digital image processing is one of the important aspects in the world of technology, especially in improving image quality from noise interference. This study aims to analyze and compare the performance of the Median Filter and Gaussian Filter in reducing salt & pepper noise in digital images. The research process was carried out using the Python programming language and the OpenCV and NumPy libraries. The initial image was randomly noised, then processed using both types of filters. The results obtained were evaluated visually and quantitatively using the PSNR (Peak Signal-to-Noise Ratio) and MSE (Mean Squared Error) metrics. Based on the experimental results, the Median Filter was able to produce cleaner images and maintain image details compared to the Gaussian Filter. These results indicate that the Median Filter has advantages in handling salt & pepper noise. This study is expected to be a reference in selecting the right filtering method to improve the quality of digital images.
Penerapan Jaringan Saraf Buatan untuk Pengenalan Pola Tanda Tangan dalam Identifikasi Potensial Diri Menggunakan Metode Backpropagation Ferdi Frans Dirga; Lailan Sofinah Harahap; Fiqih Syahputra
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 4 No. 1 (2026): Januari : Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v4i1.892

Abstract

This study develops a computational-based system to identify individual potential through the analysis of signature patterns using Artificial Neural Networks (ANN) and the Backpropagation algorithm. The research aims to explore and examine the effectiveness of applying ANN in recognizing and identifying signature patterns that are assumed to be related to an individual’s potential. In the data processing stage, Principal Component Analysis (PCA) is employed as a dimensionality reduction and feature extraction technique to optimally obtain the main characteristics of signature images. The system performance evaluation is conducted using a total of 80 signature images, consisting of 60 training data and 20 testing data. This study analyzes two network architecture configurations, namely a model with one hidden layer and a model with two hidden layers. The experimental results show that both network configurations achieve the same accuracy level of 92.5%. These findings indicate that the use of Artificial Neural Networks with the Backpropagation algorithm is effective in producing high accuracy in the signature pattern recognition process. Furthermore, the developed system has broad potential applications in the field of personal identification, such as employee evaluation, selection systems, and other applications across various organizational and industrial sectors.
Penerapan Jaringan Saraf Tiruan untuk Mengelolah Data Perubahan Cuaca sebagai Dasar Prediksi Kondisi Iklim Winda Yunia Purnama; Lailan Sofinah Harahap; Nur Azizah Hidayat
Saturnus: Jurnal Teknologi dan Sistem Informasi Vol. 3 No. 1 (2025): Januari: Saturnus: Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v3i1.1258

Abstract

This study aims to analyze the application of Deep Neural Networks (DNN) as an artificial intelligence approach in processing weather data to support more accurate and stable climate predictions. Increasingly unpredictable and fluctuating weather patterns demand modern analytical methods capable of capturing non-linear relationships among atmospheric variables. DNN is utilized due to its ability to learn complex data structures through multilayer representations that extract deeper features from input variables. Weather data such as temperature, humidity, rainfall, air pressure, and wind speed are processed through several preprocessing stages to ensure optimal model performance. This research employs a descriptive qualitative method based on literature studies to examine the role of DNN in weather prediction systems. The findings indicate that DNN demonstrates strong generalization abilities, robustness to fluctuating data, and more stable predictive outputs compared to conventional statistical approaches. Thus, DNN is considered a promising component for the development of early warning systems and modern data-driven climate analysis, offering improved reliability in understanding and forecasting atmospheric conditions.
Implementasi Jaringan Syaraf Tiruan untuk Menentukan Penutupan Kompetensi Keahlian SMK berdasarkan Minat Siswa Alisya Alfina Rizki Ritonga; Lailan Sofinah Harahap; Cici Pratiwi
Saturnus: Jurnal Teknologi dan Sistem Informasi Vol. 3 No. 2 (2025): April : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v3i1.1276

Abstract

The development of vocational education requires Vocational High Schools (SMK) to align their competencies with student interests and industry needs. However, a mismatch between student interests and the competencies offered can result in low enrollment, requiring schools to consider closing certain programs. This study proposes the application of Artificial Neural Networks (ANNs) as a predictive method to determine the potential closure of vocational competencies based on an analysis of student interest patterns. The data used includes interest history, academic grades, and other preference indicators, which are then subjected to a preprocessing stage to ensure the quality of the model’s input. The ANN is trained to accurately recognize interest patterns, thus generating objective and adaptive decision-making recommendations. The results show that the ANN implementation provides high accuracy in predicting student interest trends and provides more precise The development of vocational education in Vocational High Schools (SMK) requires the ability to align skill competencies with students' interests and industry needs. A mismatch between students' interests and the competencies offered can lead to low interest in certain programs, which in turn may result in the decision to close those programs. This study proposes the application of Artificial Neural Networks (ANN) as a predictive method to determine the potential closure of skill competencies based on the analysis of students' interest patterns. The data used includes interest history, academic grades, and other preference indicators. This data is processed through a preprocessing stage to ensure the quality of input for the model. The ANN is trained to accurately recognize students' interest patterns, allowing it to generate more objective and adaptive decision recommendations. The results of the study show that the application of ANN has high accuracy in predicting students' interest trends and provides more precise recommendations compared to traditional methods. Therefore, this system can be an effective tool for schools to plan curriculum policies more strategically and sustainably, as well as support decisions regarding skill programs that align with students' interests and industry needs.  
Implementasi Jaringan Syaraf Tiruan dalam Peramalan Harga Cpo Menggunakan Backpropagation Eva Andini; Lailan Sofinah Harahap; Siti Nurjanah
Saturnus: Jurnal Teknologi dan Sistem Informasi Vol. 4 No. 1 (2026): Januari : Saturnus: Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v4i1.1410

Abstract

This study examines the development of a Crude Palm Oil (CPO) price forecasting model using an artificial neural network algorithm, specifically the backpropagation algorithm. As one of Indonesia’s main export commodities, CPO has a significant economic impact and influences the income of oil palm farmers. The CPO price data used in this study were obtained from CIF Rotterdam, covering the period from January 2019 to December 2023. The research methodology consists of several stages, including data collection, preprocessing, model design, and model implementation using Python programming. The training results of the backpropagation algorithm show an error value of 0.537829578 after 1,000 epochs, while the evaluation using Mean Squared Error (MSE) indicates an MSE of 0.022709 during the training process and 0.017604 during the testing process. The model also produces CPO price predictions for the next three months, namely 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model is capable of predicting future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.
PENINGKATAN AKURASI PREDIKSI PENJURUSAN SISWA SMK DENGAN OPTIMASI JARINGAN SYARAF TIRUAN BACKPROPAGATION Muhammad Nabhan Akbar Marpaung; Lailan Sofinah Harahap; Fajar Al Fahri
JOURNAL SAINS STUDENT RESEARCH Vol. 4 No. 1 (2026): Februari
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v4i1.7975

Abstract

The development of artificial intelligence (AI) technology has had an increasingly significant impact on various industries, including education, particularly in terms of data processing and decision making. However, in reality, students' choice of major is often determined without proper and measurable analysis, which means that students' potential is not always in line with their chosen major. The mismatch between academic abilities and chosen fields of study is one of the problems arising from this situation. To address this issue, this study predicts majors based on subject grade data using Artificial Neural Network techniques and the Backpropagation algorithm. Backpropagation was chosen because it can produce more accurate predictions by gradually learning data patterns through a directed learning process. This approach significantly improves prediction accuracy based on model training and testing results, making it a useful tool for more objective, flexible, efficient, adaptive, and data-driven decision-making in optimally selecting majors for students to support their overall and sustainable academic success.
Analisis Performa Transfer Learning Menggunakan MobileNetV2 untuk Klasifikasi Citra X-Ray Paru-Paru M Choirul Amri; Lailan Sofinah Harahap; Abdul Rasyid
JOURNAL SAINS STUDENT RESEARCH Vol. 4 No. 1 (2026): Februari
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v4i1.8306

Abstract

Pneumonia is a lung disease that requires early detection to prevent serious complications. Chest X-ray images are widely used for diagnosis; however, their interpretation still depends on medical experts. This study aims to analyze the performance of transfer learning using MobileNetV2 for classifying chest X-ray images. The Chest X-Ray Pneumonia dataset from Kaggle was used and divided into 75% training data, 15% validation data, and 10% testing data. Image preprocessing included resizing, pixel normalization, and data augmentation. The model was trained for 20 epochs using the Adam optimizer. Experimental results achieved an accuracy of 95.40%, precision of 95.62%, recall of 95.40%, and an F1-score of 95.46%. These results indicate that MobileNetV2 provides effective and stable performance for chest X-ray image.
PREDIKSI HASIL BELAJAR BERDASARKAN METODE BELAJAR SISWA DENGAN MENGGUNAKAN JARINGAN SYARAF TIRUAN Cici Melisma; Lailan Sofinah Harahap; Henni Rosliana Pulungan
Jurnal Intelek Dan Cendikiawan Nusantara Vol. 2 No. 6 (2025): Desember 2025 - Januari 2026
Publisher : PT. Intelek Cendikiawan Nusantara

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

Abstract

Prestasi akademik siswa dipengaruhi oleh sejumlah elemen, dan salah satunya adalah cara belajar. Setiap individu siswa memiliki kecenderungan dalam cara belajar yang bervariasi, seperti visual, auditorial, dan kinestetik. Perbedaan ini bisa digunakan untuk meramalkan prestasi belajar dengan pendekatan yang didasarkan pada kecerdasan buatan. Penelitian ini menerapkan metode Jaringan Syaraf Tiruan tipe Multi Layer Perceptron (MLP) untuk melakukan prediksi prestasi belajar berdasarkan gaya belajar dan nilai akademik siswa. Data yang digunakan berjumlah 210 data siswa madrasah, terdiri dari 150 data latih dan 60 data uji. Model JST dibangun dengan empat neuron input, tiga neuron pada lapisan tersembunyi, dan satu neuron keluaran. Pelatihan dilakukan menggunakan algoritma backpropagation dengan 300 epoch dan laju pembelajaran 0,1. Hasil penelitian menunjukkan bahwa model menghasilkan akurasi prediksi sebesar 92% pada data uji. Temuan ini menunjukkan bahwa JST mampu mengenali pola gaya belajar dan hubungannya dengan prestasi belajar, serta dapat dijadikan alternatif model prediksi dalam lingkungan pendidikan.
Penerapan Jaringan Saraf Tiruan dengan Metode Backpropagation untuk Memprediksi Curah Hujan di Kota Medan Tiara Bela Harahap; Lailan Sofinah Harahap; Naina Nazwa Hasibuan
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 4 No. 1 (2026): Januari : Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v4i1.934

Abstract

Rainfall is a crucial factor in the stability of the Earth's ecosystem and has a significant impact on agriculture, forestry, energy, and water management. However, increasingly unstable climate change makes rainfall patterns difficult to predict accurately using traditional methods. The city of Medan, the capital of North Sumatra Province, has a tropical rainforest climate with an average annual rainfall of approximately ±2200 mm and an average temperature of 27°C. Significant weather fluctuations in this area can trigger flooding when rainfall increases and cause water shortages when rainfall decreases (BMKG, 2021). Therefore, a prediction approach that can manage non-linear and dynamic data is needed. Artificial Neural Networks (ANN) are one of the reliable machine learning methods for detecting data patterns. By using the backpropagation algorithm, the model can gradually reduce prediction errors, making it widely used in weather forecasting applications. In this regard, this study uses ANN with the backpropagation method to forecast monthly rainfall in Medan City by utilizing data from 2022–2024 as training and testing data.
Co-Authors Abdul Rasyid Adinda Tarisyah Hsb Alisya Alfina Rizki Ritonga Alwi Andika Panggabean Arizka Anggraini Cici Melisma Cici Pratiwi Ela Khairani Br. Siregar Eva Andini Fajar Al Fahri Fajar Syakbani Ferdi Frans Dirga Fiqih Syahputra Henni Rosliana Pulungan Intan Widya Saputri Nst juwita sari M Choirul Amri Mazayah Tsaqofah Muhammad Nabhan Akbar Marpaung Naina Nazwa Hasibuan Nasution, Mansalwa Utama Nazwa Alya Faradita Nur Azizah Hidayat Nurhidayati - Puji Sri Alhirani Rani Chantika Rifdah Syahputri Rika Khairani Riko Prananda Prayugo SITI NURJANAH Tia Ramadani Tiara Bela Harahap Warda Hamidah Winda Yunia Purnama