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INDONESIA
KOMPUTIKA - Jurnal Sistem Komputer
Published by Universitas Komputer Indonesia
ISSN : 22529039     EISSN : 26553198     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Jurnal Ilmiah KOMPUTIKA adalah wadah informasi berupa hasil penelitian, studi kepustakaan, gagasan, aplikasi teori dan kajian analisis kritis di bidang kelimuan bidang Sistem Komputer.
Arjuna Subject : -
Articles 218 Documents
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Perbandingan Kinerja Metode Linear Regression, LSTM dan GRU Untuk Prediksi Harga Penutupan Saham Coco-Cola Silalahi, Rosalia Natal; Muljono, Muljono
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.12265

Abstract

In the stock market, making predictions about stock price movements is crucial for traders, as this will affect their potential profits or losses. The accuracy of the prediction results largely depends on the method used and the quality of the data available. Therefore, this research chooses the subject of predicting the stock price of Coco-Cola. This research will conduct a comparison between several different time series data analysis methods. These methods include Linear Regression, LSTM, and GRU. The comparison of the three methods with window-width variations of 3, 4, and 5 provides an in-depth insight into the performance of each model. The comparison results show that the model achieves the best performance when using window-width=3 in the Linear Regression method. Linear Regression with MSE of 0.24, RMSE of 0.49, shows better performance compared to LSTM (2.72 & 1.65) and GRU (0.31 & 0.55). This research provides valuable guidance for future predictive model development, with a focus on improving the accuracy and precision of stock price predictions.
Analisis Sentimen Penghapusan Skripsi sebagai Tugas Akhir Mahasiswa Menggunakan Metode Multi-Layer Perceptron Makmur, Haerunnisya; Wulandari, Wulandari; Surianto, Dewi Fatmarani; Fajar B, Muhammad
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.12402

Abstract

Indonesia has levels of education where one of them is undergraduate education. There are requirements that must be done to get a bachelor's degree, one of which is to complete a final project in the form of a thesis. Nadiem Makarim, Minister of Education, Technology and Higher Education in his speech announced a new policy in the field of education regarding the non-obligation for students to prepare a thesis as a requirement for graduation. Based on this, there are pros and cons from the community, the sentiment analysis process related to this is needed. This research aims to map public sentiment contained in TikTok and YouTube social media related to the elimination of thesis using the MLP method. The stages carried out consist of observation, data collection, labeling, data normalization, preprocessing, data partitioning, TF-IDF weighting, classification, and evaluation. The accuracy obtained at the preprocessing scenario stage is 86% with case folding and stemming scenarios. Furthermore, this scenario is used in testing based on data partitioning where the highest accuracy results are obtained with a portion of 90% training data and 10% test data. The accuracy obtained is 94%.
Optimizing Career Choices in the World of Programming: A Web-Based Decision Support System with the Simple Additive Weighting (SAW) Method Akbar, Mohammad Arsan; Rusli, Risvan; Wahid, Yokogeri Abdullah; Surianto, Dewi Fatmarani; Adiba, Fathiah
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.12404

Abstract

This study proposes the development of a web-based Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to help students choose a career in programming. By integrating data from online questionnaire surveys and observations, this research highlights the complexity of career choice in the world of programming. Criteria such as salary, work location, and educational requirements were identified as key factors in decision-making. The SAW method was chosen because of its ease of understanding, flexibility, and ability to handle complex problems. The system implementation process involves data collection, observation, web-based system design, and website development. The final results show that alternative A3 (Software development) received the highest preference weight, confirming it as the best choice based on the specified criteria. The use of DSS is expected to provide effective guidance for students in making more informed career decisions.
Tree-based Ensemble Machine Learning for Phishing Website Detection Fadhilah, Husni; Maulana, Diky Restu; Utari, Rahayu
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.12495

Abstract

Phishing remains a prevalent and perilous cyber threat in the digital age, exploiting human vulnerabilities to deceive individuals into disclosing sensitive information. This paper presents a method to achieve high accuracy in phishing website detection using Tree-based Ensemble Machine Learning techniques. Through rigorous experimentation and evaluation, we identified RandomForest and ExtraTrees as the top-performing models, achieving accuracy, precision, recall, and F1 scores all exceeding 98%. Additionally, our study highlights the significance of feature selection techniques in enhancing model performance, with thresholding methods proving effective in retaining relevant features for classification. By addressing imbalanced datasets and optimizing hyperparameters, our models demonstrate robust detection capabilities against phishing attacks. These findings contribute to the advancement of cybersecurity measures and underscore the potential of ensemble machine learning in combatting online threats, ultimately enhancing internet user security.
Perbandingan Algoritma Decision Tree dan K-Nearest Neighbor untuk Klasifikasi Serangan Jaringan IoT Nafis, Zishwa Muhammad Jauhar; Nazilla, Rahmatun; Nugraha, Rega; Shofwatul ’Uyun, Shofwatul ’Uyun
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.12609

Abstract

As the number of uses of the Internet of Things continues to increase and expand. Security threats on IoT networks are also increasing. There are several techniques applied to overcome this security threat. One of them is a technique to classify an activity that is included in an attack or not along with the type of attack. Machine learning can be utilized for this classification process. Among the machine learning algorithms that can be used for this research are the Decision Tree and K-Nearest Neighbor algorithm approaches. This research aims to get the best classification results to detect the type of IoT network attack in both binary classification and multiclass classification. This research utilizes the Edge-IIoTset Cyber Security Dataset of IoT & IIoT. The results of the evaluation values obtained show that the performance of the Decision Tree algorithm is better than the KNN algorithm. With the difference in precision, recall, F1-score, and accuracy values are 0.15, 0.18, 0.17 and 0.08 in binary classification, respectively. While in multiclass classification, the difference value between the two algorithms is 0.26, 0.20, 0.22, and 0.23 respectively for precision, recall, F1-score, and accuracy
Identifikasi Kesehatan Daun Tanaman Padi Menggunakan Klasifikasi Biner Sehat dan Tidak Sehat dengan Algoritma Convolutional Neural Network (CNN) Di Kabupaten Klaten Azizah, Shelvi; Pradana, Afu Ichsan; Hartanti, Dwi
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.12771

Abstract

Rice is a vital food crop in Indonesia, where Klaten has become one of the main rice suppliers with a production achievement of 101 thousand tons in 2020. However, the challenge faced is the attack of diseases such as blast, leaf blight, and bacterial wilt which can result in huge losses in yield if not handled effectively. To address this issue, research was conducted using Convolutional Neural Network (CNN), an algorithm commonly used for image processing. In this study, the process involved two main stages namely Feature Extraction and Fully Connected Layer, utilizing a dataset of 2400 images categorized into healthy and unhealthy classes. The results show a very high level of accuracy, with the highest accuracy reaching 0.9653 and validation accuracy reaching 0.8125, as well as low loss with a total of 20 epochs. Through CNN technology, this research makes an important contribution to monitoring the health of rice plants in Klaten Regency, Indonesia, which is expected to help increase productivity and reduce crop losses.
Perancangan Alat Pemantauan Berkelanjutan Kualitas Udara Dalam Ruangan Sholahudin, Sholahudin; Yudono, Muchtar Ali Setyo; Suryana, Anang
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.13413

Abstract

Indoor air pollution is a significant threat because most human activities take place indoors. Several previous studies have proposed indoor air quality monitoring systems. Based on the literature reviewed, an opportunity identified from previous research is the change in the selection of the PM2.5monitoring sensor from GP2Y1010AUF, which is not specifically designed for PM2.5, to the ZH03B sensor, which is specifically designed for PM2.5measurement. The intersection of three air quality regulations (two national and one international) results in the measurement of four pollutants: PM2.5 (particulate matter 2.5), PM10 (particulate matter 10), NO2 (nitrogen dioxide), and CO (carbon monoxide). The system design is based on a literature review of the components, calculations, and hardware and software required. Detection data is stored on a memory card and Google Sheets, allowing users to view data history through a website published by Google Sheets. Additionally, the detection device's accuracy compared to other detectors is 93.55% for PM2.5, 93.13% for PM10, 97.10% for NO2, and 96.60% for CO.
Perancangan dan Implementasi Sistem Pengendalian Navigasi Mobile Robot Memanfaatkan Sensor Akselerometer dan Sensor Ultrasonik Hidayat, Hidayat; Khattami, Muhammad Rafli
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.13490

Abstract

Pengendalian navigasi pada mobile robot merupakan hal yang sangat penting. Navigasi dapat membantu pergerakan robot untuk bergerak dari satu lokasi ke lokasi lainnya. Pada penelitian ini dirancang suatu sistem pengendalian navigasi mobile robot dengan memanfaatkan gerakan telapak tangan. Gerakan telapak tangan tersebut berfungsi untuk menggendalikan gerakan maju, mundur, diam, belok kiri dan belok kanan pada mobile robot. Sistem yang dibangun terdiri atas dua bagian, yaitu bagian pengendali gerak dan bagian penggerak mobile robot. Pada bagian pengendali gerak terdapat Arduino Uno sebagai pemroses data, sensor akselerometer MPU-6050 sebagai pembaca kemiringan gerak telapak tangan dan modul radio frekuensi nRF24L01+ sebagai pengirim data ke bagian penggerak mobile robot. Pada bagian penggerak mobile robot terdiri atas Arduino Uno sebagai pemroses data, modul radio frekuensi nRF24L01+ sebagai penerima data, driver motor sebagai penggerak roda dan sensor ultrasonik sebagai pendeteksi obyek di depan dan belakang mobile robot. Hasil pengujian menunjukkan bahwa sistem pengendalian navigasi gerak robot dapat berfungsi dengan baik dengan jarak komunikasi maksimal sejauh 16 meter. Selain itu, mobile robot dapat berhenti ketika mendeteksi adanya obyek di depan atau belakang mobile robot pada jarak 15 cm. Model pengendalian ini dapat dikembangkan pada kondisi nyata dengan jangkauan jarak komunikasi yang lebih jauh.
Kalibrasi Sensor Monitoring Cuaca pada Area Lokal untuk Meningkatkan Akurasi pada Sensor Biaya Rendah Sugeng, Sugeng; Nizar, Taufiq Nuzwir; Jatmiko, Didit Andri; Hartono, Rodi; Kerlooza, Yusrila Yeka
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.13949

Abstract

Current weather monitoring data can be accessed in real-time through applications on Android and desktop devices provided by BMKG. Data sensors installed in specific areas or through monitoring in a particular region. BMKG sensors installed only in a limited number of areas, this study aims to evaluate and improve the accuracy of sensors used in weather monitoring systems in tropical regions. Sensor calibration is conducted using standard methods along with special adjustments for local conditions. Each sensor is tested under conditions that represent the actual environmental conditions, alongside a reference sensor for calibration. The calibration process involves collecting data from the lowest range of sensor readings to a certain range according to the sensor's or testing equipment's capabilities. The sensor readings are then compared with those of the reference measuring devices, and linear regression analysis is performed to examine the distribution of the sensor data against the actual sensor data. The calibration results yield equations that can be used to adjust the sensors to align with the actual sensors. All sensors are then installed and tested for several days to assess the calibration results. The study findings indicate an improvement in accuracy after calibration and provide recommendations for field use
Penerapan Algoritma K-Means Clustering pada Sistem Prediksi Kelulusan Tepat Waktu Mardzuki, Tati Harihayati; Lubis, Riani; Adiwijaya, Fakhrian Fadlia
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.14097

Abstract

Currently, Dikti sets a good on-time graduation percentage for study program accreditation at around 50% for each class. Therefore, the Head of Study Program requires information on the number of students predicted to graduate on time in the eighth semester later, so that policies can be taken as early as possible if the number is not as expected. The method used in this study is the K-Means Clustering algorithm, where this algorithm will divide student data into two groups (clusters), namely the number of students predicted to graduate on time and those who do not graduate on time. The data set used is student academic data from semester one to semester six with five criteria, namely GPA up to semester six, number of credits graduated up to semester six, number of semesters taken up to semester six, number of leaves up to semester six and type of school origin. The results of this study indicate that the number of students predicted to graduate on time is around 92.84% (311 students) based on the sixth semester student data set totaling 335 students with six iterations on the K-Means Clustering algorithm

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