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Arief Hidayat
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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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Deteksi Serangan Denial of Service (DoS) dan Spoofing pada Internet of Vehicles menggunakan Algoritma K-Nearest Neighbor (KNN) Ghozi, Wildanil; Rafrastara, Fauzi Adi; Sani, Ramadhan Rakhmat; Abdussalam, Abdussalam
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

The implementation of Internet of Things (IoT) technology in motor vehicles has been increasing over time and is known as the Internet of Vehicles (IoV). IoV is becoming more essential to society as it provides comfort, safety, and efficiency in driving. Unfortunately, the use of internet technology in IoV brings the potential for cyber-attacks, such as Denial of Service (DoS) and Spoofing. Intrusion Detection Systems in IoV have not yet fully matured, as this technology is still relatively new. Therefore, the potential threats and their significant impact make research on this topic urgently needed. This study aims to evaluate the performance of the k-Nearest Neighbor (kNN) classification algorithm in detecting cyber-attacks on IoV. The predicted classes in this study consist of six categories: Benign, DoS, Gas-Spoofing, Steering Wheel-Spoofing, Speed-Spoofing, and RPM-Spoofing. These two types of attacks on IoV (DoS and Spoofing) pose risks to the operational safety of vehicles, which can endanger drivers and other road users. The dataset used is a public dataset called CIC IoV2024. The performance of the kNN algorithm is also compared to three other state-of-the-art algorithms, including Naïve Bayes, Deep Neural Network, and Random Forest. The results show that k-Nearest Neighbor (kNN) achieved the best performance with a score of 98.7% for both accuracy and F1-Score metrics. kNN outperformed Naïve Bayes, which ranked second with a score of 98.1% accuracy and 98.0% F1-Score. Thus, the kNN algorithm can be recommended as a classifier in the development of an intrusion detection system for IoV
Rancang Bangun Sistem Informasi Manajemen Organisasi Kemahasiswaan Berbasis Web di UNISNU Jepara Pratama, Andrian Dico; Azizah, Noor; Sabilla, Alzena Dona
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

In today's digital era, Management Information Systems are becoming increasingly important for organizations and businesses. Management Information Systems can help organizations remain competitive and thrive in an increasingly complex and dynamic era. However, in student organizations within Unisnu Jepara, the management process is still carried out with manual practices and using paper media. Where writing on paper media requires a lot of time and energy, moreover the need for information in the organization is certainly abundant. Not to mention the risk of material misuse and loss of data in the future if not managed properly and correctly. The method used by researchers in this study is the Waterfall method which has 5 stages starting from Requirement Analysis, System and Software Design, Implementation and Unit Testing, System Testing and Integration, and Operation and Maintenance. The result of this research is the creation of an organizational management information system at Unisnu Jepara which is expected to help improve efficiency in the operational and management processes of student organizations at the Islamic University of Nahdlatul Ulama Jepara.
Implementasi Business Intelligence untuk menganalisis Perkembangan Akademik Mahasiswa di Program Studi Sistem Informasi UNISNU Jepara Margaretha, Sintikhe Novia; Azizah, Noor; Sabilla, Alzena Dona
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
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Abstract

This research aims to build a Business Intelligence (BI)-based academic monitoring system in the Unisnu Jepara Information Systems Study Program to improve the efficiency and quality of decision making. The method used involves analyzing student academic data with the help of Microsoft Power BI for data processing. Data is taken from the Academic Information System (SIAKAD) and processed to produce visualizations in the form of informative dashboards. The research results show visualization of academic data which includes the number of students, academic status, average GPA, study period, and graduate success. This dashboard makes it easier to monitor and analyze academic data, supports better decision making, and improves the quality of education in the Unisnu Jepara Information Systems Study Program. This research makes aware how important BI implementation is in optimizing academic data management and strategic decision making in
Analisis Pengaruh Media Sosial terhadap Produktivitas Akademik Mahasiswa menggunakan Metode Decision Tree dan Random Forest Murwaningtyas, Chatarina Enny; Kristiamita, Angel; Putri, Agatha Lintang Antika Ika; Puspaningrum, Fibelia Dwi; Mahanani, Carolina Dhinda Putri
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
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This study aims to evaluate the impact of social media usage on the academic productivity of Universitas Sanata Dharma Yogyakarta students, measured through their Grade Point Average (GPA). The methods employed involve two machine learning models: Decision Tree and Random Forest. The data were processed using outlier-resistant scaling techniques and data balancing through oversampling. The results show that the Random Forest model outperformed with an accuracy, precision, recall, and F1-score of 90% each. Meanwhile, the Decision Tree model achieved 80% accuracy, with a precision of 86%, recall of 80%, and F1-score of 82%. Feature importance analysis revealed that 'Faculty' and 'Gender' are the most significant factors in predicting students' GPA. This study concludes that employing Random Forest with data balancing techniques can enhance prediction accuracy and reliability, providing insights into the optimal use of social media to improve students' academic productivity.
Model Klasifikasi Emosi Berbasis Teks dengan Algoritma Decision Tree dan Support Vector Machine Raihan, Habib Aulia; Yuliansyah, Herman; Murinto
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
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Abstract

Text-based communication has become a key means of interaction across various sectors. Previous studies have applied supervised learning algorithms to emotion classification in text. These studies used different datasets, but this diversity also introduced a risk of overfitting in text-based emotion classification models. Consequently, the use of cross-validation and hyperparameter optimization is required to ensure the model’s generalization ability. The aim of this research is to compare the performance of two supervised learning algorithms—Decision Tree (DT) and Support Vector Machine (SVM)—for emotion classification on an English-language text dataset of 16,000 labeled entries (anger, fear, joy, love, sadness, surprise) sourced from Kaggle. The dataset undergoes cleaning, tokenization, stopword removal, and lemmatization, after which features are extracted using TF-IDF. Both algorithms are evaluated with K-Fold and Stratified K-Fold cross-validation, then used to compute metrics of accuracy, precision, recall, and F1-score. Classification results show that the hyperparameter-tuned DT achieved an average accuracy of 88%, while the hyperparameter-tuned SVM achieved 89%. Meanwhile, Stratified K-Fold cross-validation yielded an accuracy variance of just 0.02% for DT and 0.15% for SVM. Therefore, it can be concluded that Stratified K-Fold performs better than standard K-Fold on imbalanced datasets, and that hyperparameter-tuned SVM outperforms hyperparameter-tuned DT.
Penerapan Metode Double Moving Average Untuk Memprediksi Penjualan Tiket Bus Sinar Jaya Po Tambun Tundo, Tundo; Nugroho, Agung Yuliyanto; Saidah, Andi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Abstract

The Sinar Jaya Autobus Company (PO) is one of the buses engaged in the tourism business that sells and provides community needs such as bus tickets. This PO requires forecasting in data processing to produce accurate reports. The reason for this is because PO Bus Sinar Jaya in determining the demand for bus tickets cannot predict availability. Based on these reasons, the design of this system uses the Double Moving Average (DMA) forecasting method for the forecasting process in determining the amount and type of availability that will be sold for the following month. By using this calculation method it is hoped that the owner of PO Sinar Jaya will further optimize the things that can be detrimental to this PO in operating. If sales increase each month, using the DMA method, sales predictions for the next three months can be determined, the higher the number of ticket requests on the PO Sinar Jaya Bus, so that the forecasting results can help the PO to avoid running out of tickets according to consumer demand. Based on the research that has been carried out, it can be concluded that the Sinar Jaya PO Tambun bus ticket sales forecast using the Double Moving Average (DMA) method obtained the smallest MAPE value calculation results in order 2, namely 0.004599299 and the smallest MAPE value in order 3, namely 0.000614191. Comparison of the results of MAPE value calculations to determine the accuracy of forecasting results carried out with order 2 and order 3, it is proven that order 3 is more accurate for determining the error percentage results in this study.
Implementasi Algoritma K-Nearest Neighbor Dalam Prediksi Penyakit Jantung Ardiansyah, Arif; Juan; Sirri, Latiful; Hapsari, Rinci Kembang; Santoso, Syahrul Riza Andi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Heart failure is a serious and pressing health problem that affects millions of people worldwide. Several factors influence the occurrence of heart failure, such as age, type of pain, blood pressure, cholesterol levels, and other risk factors associated with heart disease. With current technological developments, data mining and machine learning can be used to predict patient health conditions. Therefore, the problem of this research is how to implement data mining techniques for identifying heart disease. The goal of the study is to identify heart disease and prevent heart failure. This study utilises the K-Nearest Neighbour (k-NN) algorithm to estimate the likelihood of patients experiencing heart failure based on available data features. The data used is taken from the kaggle.com site, which includes information from patients diagnosed with heart failure and those who do not suffer from heart failure. The analysis process involves data processing steps, such as normalisation, feature grouping, and selecting the optimal K parameter for the k-NN algorithm. Evaluation is carried out by calculating the accuracy, precision, recall, and F1-score values. Testing is carried out on a dataset with 299 patient data, which is divided into training data and test data with a ratio of 80:20. The results of this study indicate that the k-NN algorithm has an accuracy of 87% in predicting kidney failure. This result indicates that the k-Nearest Neighbour algorithm can effectively predict heart failure.
Implementasi Metode Rapid Application Development pada Sistem Pencatatan Laporan Pick-Up dan Delivery Pakaian berbasis Android pada Hotel XYZ Septiana, Via; winanti, winanti; Carolina, Yuanita; Nurasiah, Nurasiah; Suwita, Jaka
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
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Laundry is one of the operational facilities in hotels. Hotel guests can use the laundry service to wash their clothes when staying at the hotel. One of the guests staying at the hotel is Airline Crew from various airlines. The current laundry system is not running well because there are still problems that occur, namely hotel guests sometimes do not completely fill out the list form in the hotel room, such as not filling in the room number and incompletely filling in the clothes to be laundered and the quantity. So the laundry staff has difficulty when delivering clean clothes because there is no room number information. In addition, there are complaints from guests because the number of clothes received does not match the number of clothes originally laundered. This research produces a laundry application created using the Flutter application and the MySQL Lite database. The system created consists of 4 users: guests, laundry, checkers, and supervisors. This research uses the PIECES analysis research method, RAD (Rapid Application Deployment) development and testing using blackbox testing
Pengembangan Aplikasi Pemesanan Menu Café dengan Metode Agile winanti, winanti; Darmawan, Dwiky; Yusuf, Yusuf; Tama, Ardy Riyantai; Widiyanti, Dian; Sa’adah, Fitria; Septian, Indra; Basuki, Sucipto; Jumiran, Jumiran; Nurasiah, Nurasiah
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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The ordering system at Café XYZ is still carried out conventionally where customers must come directly to the café so that service is slow if there is a long queue. The purpose of developing a Café menu ordering application is to simplify and speed up the café menu service and check order reports that can be done in real time. The data collection method is carried out by direct observation at Café XYZZ. The application development uses the Agile method, and a website-based system. Application testing is carried out using black box testing by testing the ease of use of the application and the completeness of the features. The cafer menu ordering application really helps customers in ordering menus and makes it easier for admins in terms of reporting and can reduce the length of the queue. The application will be developed in the future on a mobile basis to provide better service and convenience to customers
Integrasi Metode Weighted Product (WP) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) untuk Pendukung Keputusan Penentuan Asisten Dosen Muthmainnah, Aindri Rizky; Pamput, Jessicha Putrianingsih; Adiba, Fhatiah; Surianto, Dewi Fatmarani; Nasrullah, Asmaul Husnah; Budiarti, Nur Azizah Eka
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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The selection of teaching assistants requires an objective and effective decision-making system. This study designs a decision support system for selecting assistants in the Algorithm and Basic Programming course at JTIK, Universitas Negeri Makassar, by integrating the Weighted Product (WP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. WP assigns weights to evaluation criteria, while TOPSIS identifies the best candidates based on positive and negative ideal distances. The criteria include academic performance, communication, subject mastery, and teaching experience. Testing results show that the system produces consistent selections, aligned with manual calculations and recruitment outcomes, proving its validity and effectiveness in supporting the selection process.

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