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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 222 Documents
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Metode System Development Life Cycle dalam Perancangan Sistem Informasi Kuliah Kerja Kemasyarakatan Hidayat, Taufik; Mobarrok, Abi; Sukisno, Sukisno; Nugroho, Asep Hardiyanto
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

Our annual activity, Community Service Internship, will be conducted this semester. Previously, this reporting activity was carried out by sending pictures via WhatsApp. So, we created a reporting system to make it more efficient. Now, the reporting process will be handled through a website and monitoring will also be done through the website. This will make it easier for both participants and mentors to carry out the activity," he said. The methodologies used are SDLC (System Development Life Cycle) for documentation and Prototype methodology for system design. ISO 9126 has been used for system testing with a functionality score of 84% and a usability score of 89.45%. So, from testing to system design to documentation, everything was done as per the requirements.
Klasifikasi Penentuan Kualitas Kayu Jati Berdasarkan Citra Digital Menggunakan Algoritma K-Nearest Neighbour Kamaruddin, Kamaruddin; Umar, Najirah; Wahyuningsih, Pujianti; Sudarsono, Firman
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

The increase in demand for goods made from wood cannot be limited, especially the demand for table furniture, cupboards and so on. Along with development, teak wood production has shifted to superior types of teak wood because the growth period is faster, but this condition means that the quality of teak wood is not like the old type of teak wood. Difficulty in seeing the quality of wood is a problem faced by craftsmen and furniture makers. The aim of this research is to determine the quality of wood species which are divided into 3 class categories, namely class A, class B and class C. To produce a classification of wood quality, researchers use the KNN method by carrying out HSV color segmentation then analyzing the color value of each image pixel based on the tolerance value on HSV color dimensions. The results of this research were using 65 teak wood training data for each class. Testing was carried out using 27 teak wood test data with an accuracy level of 85.19%, precision reaching 85.46%, recall reaching 85.18% and F1 score reaching 85.3%.
Penggunaan Machine Learning (ML) dan Natural Language Processing (NLP) untuk mendeteksi Sentimen Ancaman Siber Wijaya, Andri; Putra, Steven Adi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2025): Maret
Publisher : Universitas Wahid Hasyim

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Abstract

Cybersecurity has become a critical issue in the digital era, with evidence in the last 3 years there have been 6 cybercrime cases in Indonesia that attacked servers, one of which was the latest theft of Bank Syariah Indonesia data in May which resulted in the server being paralyzed for 5 days and the impact was that customers could not access the mobile banking application. From the various cybercrime cases that have occurred in Indonesia, we need to know the current trend of public sentiment about it and one of the sources of public sentiment is Twitter. The use of Machine Learning (ML) and Natural Language Processing (NLP) has become a major focus in understanding public sentiment contained in twitter data. This research proposes an approach that combines ML and NLP techniques to detect sentiment in tweets. The method includes a pre-processing stage to clean and transform the tweet text into a vector representation, followed by the application of ML classification model namely Naïve Bayes to identify positive, negative or neutral sentiments from the tweet dataset. This research utilizes a set of collected and annotated tweet data using python to train and test the model. The experimental results show that the proposed approach successfully produces sentiment classification with an accuracy rate of 62%. It can be concluded that the accuracy of the model is still satisfactory with a positive recall value of 74%, meaning that the public sentiment of the tweets still contains words of a positive nature.
Aplikasi E-voting Berbasis Blockchain dengan Metode Smart Contract Junaedi, Junaedi; Fernando, Albert; Hermawan, Aditiya
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

Voting is an important process in many contexts, such as in organizational decisions and the selection of leaders. With the development of technology, the concept of electronic voting emerged, where humans can cast their votes electronically. Electronic voting has become an important alternative in organizational decision making and leader selection, but often faces challenges related to security and fraud. In this study, a blockchain-based electronic voting application has been developed using the Smart Contract method. Blockchain technology was chosen because of its decentralized and public nature, which can provide a high level of security and transparency to voting data. Evaluation of the application was carried out through a questionnaire filled out by 39 respondents. The results showed a high level of agreement, with 91.46% of respondents stating that they strongly agreed with the effectiveness of this application. This indicates that the implementation of a blockchain-based electronic voting system has the potential to increase trust and integrity in the election process. In addition, this application also offers easy access and data transparency, which is expected to increase public participation in the democratic process. By continuing to conduct further research and development, this blockchain-based electronic voting application has great potential to become the standard in future elections.
Identifikasi Kesegaran Ikan Bandeng Non-kontak menggunakan MobileNetV2 Hidayatullah, Achmad Nasrul; Prasetyo, Eko; Purbaningtyas, Rani
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

Milkfish is a superior commodity in several districts in Indonesia, namely Sidoarjo, Semarang, and Banten. This fish is also a favorite of Indonesians because it is nutritious and affordable. Therefore, for milkfish processed product business people, the freshness of milkfish is an important parameter because the freshness of the fish affects the quality of the processed products. Manual fish sorting is a problem when the number of fish is vast because it is prone to errors due to fatigue. In addition, manual fish sorting is also wasteful and time-consuming. Therefore, a non-contact automatic system is needed to identify fish freshness based on digital images. This study uses the Convolutional Neural Network (CNN) model to develop an application for milkfish freshness identification. We applied the MobileNetV2 model to identify the freshness of milkfish into three freshness classes, namely very fresh, fresh, and not fresh. The application uses the MobileNetV2 model on 312 milkfish images. The freshness classification performance reached 95%, 70%, and 80% in the high-fresh, fresh, and not-fresh classes, respectively. The global accuracy of the system reached 81.6%, indicating that the application can work well. From the experiments and analysis conducted, it can be concluded that the system has good capabilities in identifying fish freshness.
Pengenalan Gestur Bahasa Isyarat Indonesia dengan Mediapipe Keypoints Dewanto, Febrian Murti; Harjanta, Aris Tri Jaka; Nada, Noora Qotrun; Herlambang, Bambang Agus
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

Difficulty in communication is an obstacle for deaf friends who cannot learn the language orally or acquire normal speech skills. The development of sign language gesture recognition technology is an important step to improve accessibility and social integration for the deaf community. The use of MediaPipe Holistic Keypoints and deep learning techniques provides significant potential in recognizing and understanding sign language gestures. The main objective of this study is to classify Indonesian Sign Language (BISINDO) gestures using MediaPipe Holistic Keypoints and a deep learning approach to identify basic words in sign language. By extracting features using mediapipe holistic and sending them to the LSTM 6 hidden layer model with 70:30 split train test and 250 epochs, an accuracy of 68% was produced. This is due to the limited number of datasets taken for the study.
Implementasi dan Perancangan Sistem Informasi Penjualan Vapestore Berbasis Mobile Flutter Ripai, Rizki; Fauzi, Yudiansyah; Sidik, Fazar; Pari, Riki Aldi; Hamdan, Rifky Aditia
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

The sale of vape products has become a rapidly growing industry, with increasing demand from a diverse range of consumers. To keep up with these developments, the existence of an efficient and easily accessible sales information system is crucial for vape store owners. This study aims to implement and design a mobile-based sales information system for Vapestore using Flutter. The system development method used in this research is the iterative software development method. Flutter was chosen as the development platform due to its ability to create mobile applications with responsive user interfaces and efficient cross-platform deployment. The designed information system will include features such as inventory management, customer management, sales report generation, and online payment integration. By using Flutter, it is expected that this application can be easily accessed through various mobile devices, such as smartphones and tablets, allowing store owners to manage their sales efficiently wherever and whenever needed. Additionally, with the online payment integration feature, customers will also experience a more convenient and faster shopping process. This research is expected to provide a positive contribution to vape store owners in improving their operational efficiency and enhancing the customer shopping experience. Furthermore, the results of this study may serve as a reference for future research in the development of more advanced sales information systems that are tailored to the continuously evolving market needs.
Implementasi Profile Matching pada Sistem Pendukung Keputusan Seleksi Peserta Tenda Kewirausahaan Setiawan, Aries; Nuryanto, Imam; Mintorini, Ery; Hidajat, Moch. Sjamsul; Farida, Ida; Widjajanto, Budi; Prasetya, Jaka; Lewa, Andi Hallang; Karmila, Karmila
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

One of the programs from the Entrepreneurship Unit is the entrepreneurial tent participant program. In the manual assessment process, participant selection is only based on the type of entrepreneurial product that will be offered. However, this does not get maximum selection results because if the existing selection only uses one variable component and the assessment tends to contain elements that have no potential. One decision making method that has weight in its calculations is profile matching. Profile matching works by assigning a standard value to each variable and a weighted value is also assigned to the variable. Next, look for differences in participant scores and standard scores for each variable. The ranking results resulting from profile matching are a combination of several variables with different weight levels. Therefore, in assessing the selection of entrepreneurial tent participants, it is best to use the existing calculation pattern using the profile matching method. The weight of each variable is determined by the decision maker, in this case the head of Entrepreneurship. With different percentage weight values ​​for each variable, it will provide assessment results that are in accordance with the level of competency of the entrepreneurial tent selection participants.
Model Hybrid Random Forest dan Information Gain untuk meningkatkan Performa Algoritma Machine Learning pada Deteksi Malicious Software Rafrastara, Fauzi Adi; Ghozi, Wildanil; Sani, Ramadhan Rakhmat; Handoko, L. Budi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

The evolution of malware, or malicious software, has raised increasing concerns, targeting not only computers but also other devices like smartphones. Malware is no longer just monomorphic but has evolved into polymorphic, metamorphic, and oligomorphic forms. With this massive development, conventional antivirus software is becoming less effective at countering it. This is due to malware's ability to propagate itself using different fingerprint and behavioral patterns. Therefore, an intelligent machine learning-based antivirus is needed, capable of detecting malware based on behavior rather than fingerprints. This research focuses on the implementation of a machine learning model for malware detection using ensemble algorithms and feature selection to achieve optimal performance. The ensemble algorithm used is Random Forest, evaluated and compared with k-Nearest Neighbor and Decision Tree as state-of-the-art methods. To enhance classification performance in terms of processing speed, the feature selection method applied is Information Gain, with 22 features. The highest results were achieved using the Random Forest algorithm and Information Gain feature selection method, reaching a score of 99.0% for accuracy and F1-Score. By reducing the number of features, processing speed can be increased by almost fivefold.
Penerapan Recursive Feature Elimination (RFE) pada Tree-Based Classifier untuk Identifikasi Risiko Diabetes Maori, Nadia Annisa; Azizah, Noor
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

Diabetes mellitus is a common chronic disease with significant global impact. Early identification of individuals at high risk of developing diabetes is critical for the prevention and management of the disease. This study explores the use of Recursive Feature Elimination (RFE) in decision tree-based classifiers to improve the accuracy of diabetes risk prediction. The Pima Indians Diabetes Database (PIDD) dataset was used as the database, and algorithms such as Decision Tree, Random Forest, Gradient Boosting, and Xtreme Gradient Boosting were tested. The results showed that the application of RFE improved the model accuracy, with Random Forest and Gradient Boosting achieving the highest accuracy of 77.27%. RFE also successfully identified the most relevant features, reduced the risk of overfitting, and improved model interpretability. This study provides a strong foundation for the development of more effective predictive tools in diabetes management and prevention. Future studies are recommended to test the generalizability of this approach to a wider dataset and in various clinical contexts.

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