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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 8 Documents
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Search results for , issue "Vol. 7 No. 2 (2025): September" : 8 Documents clear
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
Publisher : Universitas Wahid Hasyim

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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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Abstract

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
Publisher : Universitas Wahid Hasyim

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Abstract

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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Abstract

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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Abstract

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.
Perbandingan Apache Airflow dan Apache Spark dalam Proses ETL untuk Memprediksi DropOut dan Keberhasilan Akademik Mahasiswa Laksono, Triyan Agung; Andriyani, Widyastuti
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Abstract

Dropout prediction in higher education is important because it impacts the academic success of students and the overall effectiveness of educational institutions. This research aims to build an automated ETL pipeline using Apache Airflow and Apache Spark to process academic data and predict student graduation status. The dataset used consists of 4,424 samples with 36 features covering demographic, academic, and socio-economic attributes. The data is processed through the stages of extraction, transformation (including SMOTE normalization), with loading into the Random Forest model. The evaluation results showed an accuracy of 62.93% and the highest ROC-AUC value of 0.81 for the dropout class. The Airflow pipeline excels in task scheduling efficiency, while Spark is effective for large-scale data processing. This approach shows practical potential in supporting early warning systems for academic policy decision-making. This research contributes to the intergation of big data and machine learning technologies for efficient and automated higher education data processing.
Comparative Study of Recurrent Neural Network (RNN) and Extreme Learning Machine (ELM) in Predicting Bank Central Asia’s Stock Price Mukharomah, Rizanatul; Siswanah, Emy
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Abstract

Predicting stock prices is an important financial topic, especially for investors who want to maximize profit and minimize risk. This research compares two machine-learning capabilities, a Recurrent Neural Network (RNN) and an Extreme Learning Machine (ELM), in predicting Bank Cental Asia (BBCA) stock prices. These two are chosen for their capabilities in handling time-series data. This research uses the data of BBCA’s daily prices over a certain period and involves several steps such as data collecting, data pre-processing, model training, and calculation of accuracy value. This accuracy calculation will be evaluated using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). This research shows ELM has better accuracy than RNN in predicting BBCA’s stock prices. ELM shows lower MSE and MAPE values than RNN, indicating the capability of ELM to predict with smaller errors. This research also concludes ELM is better in accuracy than RNN in predicting BBCA’s stock prices. Thus, ELM is the recommended method to predict stock prices.

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