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Arief Hidayat
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INDONESIA
Jurnal Informatika dan Rekayasa Perangkat Lunak
Published by Universitas Wahid Hasyim Semarang
ISSN : 26562855     EISSN : 26855518     DOI : http://dx.doi.org/10.36499/jinrpl
Core Subject : Science,
Computer Science & IT
Journal of Informatics and Software Engineering accepts scientific articles in the focus of Informatics. The scope can be: Software Engineering, Information Systems, Artificial Intelligence, Computer Based Learning, Computer Networking and Data Communication, and Multimedia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 271 Documents
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Sistem Prediksi Penggunaan Bahan Baku Berbasis Odoo pada UMKM Permak dan Jahit Hafizh menggunakan Metode Least Square Nur Wakhidah; Bagoes Ardianjar
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a significant role in economic development, especially in developing countries like Indonesia. In addition to supporting the economy, MSMEs help alleviate unemployment. Hafizh Permak and Jahit, an MSME in Semarang City, faces challenges in managing raw material usage, which can increase production costs and the risk of overstocking or understocking. This study aims to develop a web-based raw material usage prediction system using Odoo and the least square method, based on raw material usage data from Hafizh Permak and Jahit from January 2022 to December 2023. The least square method is applied to 20 types of raw materials. The study results demonstrate the ability to predict raw material usage with an average MAPE of 8.2%, indicating excellent predictive accuracy. Besides predicting raw material usage, this research supports stock management and guides the raw material purchasing process for Hafizh Permak and Jahit in subsequent months.
Perancangan Platform E-Commerce Berbasis Website menggunakan Metode Waterfall sebagai Media Pembelajaran Generasi Z Rizal Furqan Ramadhan; Kunti Eliyen; Halimahtus Mukminna
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2025): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v7i1.12280

Abstract

Nowadays, online buying and selling transactions have become a habit and lifestyle for Indonesian people. Online-based buying and selling transaction activities make it easier and faster for people in terms of service and convenience. One of the services in question is the practicality of sending goods and not requiring buyers to go to the seller's location. Generation Z is one of the generations born with the advancement of information technology. Therefore, it is necessary to develop a learning module that explains the design or development of a Website-based E-Commerce platform using the Waterfall method. The Waterfall method is used as a guide for researchers in developing an E-Commerce website design consisting of several stages. While the final result of the website design will be presented in the form of a module accompanied by an installation process to facilitate understanding of Generation Z in implementing an E-Commerce website. This study involved 2 experts, including academics and practitioners who know about the digital economy and informatics. The involvement of experts in the validation session by applying the Likert scale as a measurement scale. The results of the E-Commerce design validation can be said to be good so that it needs to be developed in a further stage. Testing on the E-commerce design made using the Black Box Testing technique to measure the suitability of the features contained in the E-Commerce design.
Analisis Perbandingan Formula Haversine dan Formula Vincenty dalam Perhitungan Jarak Destinasi Wisata Kabupaten Pacitan Moh. Fauzi; Bambang Purnomosidi Dwi Putranto
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2025): Maret
Publisher : Universitas Wahid Hasyim

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Abstract

In the development of geographic information technology, particularly in the tourism sector, accurate distance determination between two coordinate points has become very important. This research aims to compare the accuracy level and computational time efficiency between the Haversine Formula and Vincenty Formula. The method used is by testing distance calculations between user location and 15 tourist destinations in Pacitan Regency using both formulas. The test results show that both methods have high accuracy levels with an average difference of only 23.4 meters. However, in terms of computational performance, the Haversine Formula proves to be superior with processing times nearly three times faster than the Vincenty Formula. These findings indicate that the Haversine Formula is a more optimal choice for implementing tourist destination distance determination systems that require real-time computation or large-scale data processing.
Ekstraksi Palet Warna untuk Kompresi Gambar Digital menggunakan Algoritma K-Means Ahmad Ma'ruf; Hidayatus Sibyan; Nahar Mardiyantoro
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2025): Maret
Publisher : Universitas Wahid Hasyim

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Abstract

The growing needs for efficient image data compression have been driven by rapid technological advancement. This study evaluates the effectiveness of the K-Means algorithm for digital image compression through dominant color palette extraction and subsequent color quantization for image reconstruction. The research investigates how the number of clusters (K) affects both compression ratio and Peak Signal to Noise Ratio (PSNR). An experimental approach was implemented, compressing 24-bit RGB images at various resolutions (VGA, SVGA, HD, FHD) using different cluster quantities (8, 16, 32, 64, 96, 128). Through descriptive and correlation analyses, relationships between cluster numbers, compression ratio, Mean Squared Error (MSE), PSNR values, and processing time were examined. Results demonstrate that the K-Means algorithm achieves effective image compression, with an average compression ratio of 67%, MSE of 0.00077, PSNR of 81.43 dB, and processing time of 0.73 seconds. Compression quality was strongly influenced by cluster quantity, with both PSNR values and compression ratios improving as cluster numbers increased. The research determined that 96 clusters represents the optimal configuration, delivering high-quality compression with reasonable computational efficiency
Pengaruh Konten YouTube terhadap Penerimaan Mahasiswa Baru: Studi Analisis Sentimen BERT dan Korelasi Spearman Edi Widodo; Eka Putri Rachmawati; Pratama Buana
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Abstract

This study aims to analyze the relationship between the number of videos, views, positive comments, negative comments, and neutral comments with the number of new students at higher education institutions during 2021–2024. The data were obtained from three sources: (1) quantitative data on the number of new students from LLDIKTI, (2) data on the number of videos and views from YouTube, and (3) sentiment data from comments classified using the BERT algorithm into positive, negative, and neutral categories. The research employed a quantitative approach using the Random Forest regression model to evaluate the influence of independent variables, namely, the number of videos, views, and sentiment on the dependent variable, which is the number of new students. The analysis results showed a significant positive correlation between the number of views and positive sentiment with the number of new students, while negative sentiment had a negative correlation. However, this relationship is not entirely linear, as indicated by an R² value of 32.1%, suggesting the possibility of other influencing factors. Spearman correlation analysis also confirmed a strong relationship between the number of video, views and positive sentiment with the number of new students, with a correlation value of 0.7-0.8. These findings highlight the importance of digital marketing strategies, such as increasing publications, views and creating content that generates positive sentiment, to attract more new students.
Klasifikasi Tingkat Demam Berdarah menggunakan Metode Naive Bayes Classifier untuk Deteksi Dini Akhmad Pandhu Wijaya; Gilar Pandu Annanto; Adek Saputra
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2025): Maret
Publisher : Universitas Wahid Hasyim

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Abstract

Nowadays, the flow of information has experienced a significant increase every day, which results in the accumulation of data in the form of text documents both online and offline. The classification of Dengue Fever data in the medical field is an essential task in predicting the disease, it can even support doctors in establishing a diagnosis, so it is important to make a diagnosis quickly in order to reduce the risk of Dengue Fever spreading in the community. The classification of dengue fever level using Naïve Bayes Classifier for early detection is the Naïve Bayes Algorithm can be used to classify the level of DD (Dengue Fever), and DBD1 (Dengue Hemorrhagic Fever Level 1), DBD2 (Dengue Hemorrhagic Fever Level 2), DBD3 (Hemorrhagic Fever) Level 3), Dengue4 (Hemorrhagic Fever Level 4) for early detection, which is taken from the result of the largest Naïve Bayes probability value. In testing the Naïve Bayes method using test data as many as 60 data.
Deteksi Jenis Jerawat Berbasis Android menggunakan Ensemble Deep Learning dengan Optimasi Layer Parsial Hanggoro Aji Al Kautsar; Waeisul Bismi; Deny Novianti; Muhammad Qomaruddin
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Abstract

Accurate classification of acne types is essential for determining the appropriate treatment. This study developed an automatic detection system based on Android using an ensemble Deep Learning approach and partial layer optimization to address the visual similarity challenges among different acne types. MobileNet and EfficientNetB0 were used as base models, then combined using majority voting and weighted averaging techniques. The dataset used consisted of 6,875 acne images that underwent preprocessing and augmentation. To improve efficiency and prevent overfitting, early layers of the models were frozen, and fine-tuning was applied only to the top layers. The best-performing model was then converted into TensorFlow Lite (TFLite) format and integrated into an Android application. The application allows users to classify acne in real time using either the camera or gallery. Evaluation results showed that the ensemble model offered better accuracy and stability compared to individual models, with fast inference times on Android devices. This system provides a practical and accurate solution for both general users and medical professionals in detecting acne types.
Pemilihan Dosen Pembimbing Berdasarkan Judul Skripsi Mahasiswa menggunakan Metode Cosine Similarity Ratri Andinisari; Fathur Riski; Karina Auliasari
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Abstract

The process of selecting a thesis advisor that aligns the thesis topic with the advisor's area of expertise will greatly assist in the thesis preparation process for students. However, in reality, the process of determining the thesis advisor is often not based on the advisor's area of expertise. The process of determining the thesis advisor is often done manually and inefficiently, which can lead to a mismatch between the advisor's area of expertise and the student's thesis topic. From this problem, this research developed a recommendation model for selecting thesis supervisors using the Cosine Similarity method, utilizing thesis title data and the list of lecturers' areas of expertise. The process begins with preprocessing the text data of thesis titles, then calculating word weights using TF-IDF and measuring similarity to finally match the students' thesis titles with the lecturers' areas of expertise, which are calculated using the Cosine Similarity algorithm. From the results of testing the process of matching research topics from student thesis titles with the expertise and research of lecturers, it was produced accurately, as indicated by a precision value of 81% and a recall of 90%. This result also shows that the relevance level of the recommendations is very high. The F1 score obtained, which is 85%, indicates that the modeling has a good balance between precision and sensitivity (recall).
Comparative Analysis of Classification Models for Competencies Certification: A Data Mining Approach ADITYA CAHYA SAPUTRA; IMAM YUADI
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Abstract

This study elaborates the application of various machine learning (ML) algorithm models to identify the classification of employee competency to take the competency certification needed to support the work. The goal is to choose the best ML model to improve accuracy, scalability, and fairness. The algorithms to be tested in this study are Logistic Regression, SVM, KNN, and Naive Bayes as traditional algorithms with Random Forest as an ensemble algorithm. This study took data from 13,830 employees who had been submitted in competency certification activities in 2024 at PT PLN (Persero). All models were measured through cross-validation on parameters such as accuracy, precision, recall, and F-1 score using the Python programming language on Jupyter Notebook. The best performing model on the F-1 score parameter was Logistic Regression which achieved the highest score of 0.9559. Meanwhile, Random Forest is the best model on the Precision parameter which is very important to identify employees who are truly incompetent to avoid losses due to human error. Based on this research, Logistic Regression and Random Forest can be prioritized to improve the accuracy of employee competency status in order to produce truly competent employees to strengthen operational performance and increase company revenue.
Pengenalan Karakter Tulisan Tangan pada Dokumen Berita Acara Bimbingan Skripsi menggunakan Preprocessing dan YOLOv8 Arifah Nur Ainia; Nanik Suciati; Hudan Studiawan
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Abstract

Handwritten character recognition is one of the challenges in the field of digital image processing, especially in academic documents such as thesis guidance minutes. This study aims to compare the performance of the YOLOv8 model in detecting handwritten characters on two types of datasets, namely original images and preprocessed images. Preprocessing is carried out through the stages of grayscale, CLAHE, Gaussian blur, adaptive thresholding Gaussian, dilation, and erosion. Labels in the preprocessed data are obtained by copying annotations from the original data without adjusting for visual changes. Both datasets were trained using YOLOv8s for 30 epochs. The evaluation results show that the model trained on the original data gives the best results with mAP@0.5 of 0.795 and mAP@0.5:0.95 of 0.606, while the model trained on the preprocessed data only achieves mAP@0.5 of 0.748 and mAP@0.5:0.95 of 0.560.

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