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INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 30 Documents
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Search results for , issue "Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi" : 30 Documents clear
IoT Based Monitoring System Using MQTT Protocol on Tortilla Chips Cutting Machine Thayyib, Ahmad Reza; Salamah, Irma; R.A., Halimatussa’diyah
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3328

Abstract

This research focuses on monitoring and controlling a tortilla chip-making machine using the MQTT protocol. Tortilla chips are a popular snack made from corn tortillas that are cut into triangles and fried or baked. The study utilizes the Internet of Things concept, where sensors are integrated into the machine to enable data exchange and communication through networks. The MQTT protocol is chosen due to its lightweight nature, making it suitable for resource-constrained devices and efficient in IoT applications. The research involves using ESP32 as the microcontroller and various sensors, such as ZMPT101b, ACS712, Tachometer, and HC-SR04. The data collected from the sensors is transmitted to a Thingspeak channel via MQTT, allowing real-time monitoring and control of the machine. The results show that the MQTT protocol effectively facilitates communication of the tortilla chip cutting machine, with satisfactory delay and data integrity. The tortilla chip cutting test was successful, producing triangular-shaped chips. Overall, the research concludes that the implementation of the MQTT protocol in the IoT-based tortilla chip-making machine is effective and reliable. The results of this study indicate that the average delay in data communication is 2.8 seconds, and the integrity testing revealed a 3% error in the accuracy of the sensor data.
Optimasi Fitur dengan Forward Selection pada Estimasi Tingkat Obesitas menggunakan Random Forest Alpiansah, Agung Bia; Ramdhani, Yudi
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3125

Abstract

Obesitas remaja di Indonesia sedang meningkat, karena kebiasaan makan yang buruk dan gaya hidup yang kurang gerak. Obesitas meningkatkan risiko masalah kesehatan yang serius seperti penyakit jantung, stroke, diabetes, dan lain-lain yang memerlukan tindakan segera. Obesitas berkembang ketika jumlah kalori yang dikonsumsi melebihi jumlah kalori yang dibakar. Obesitas telah menjadi masalah kesehatan masyarakat yang sangat besar di seluruh dunia. Menurut Organisasi Kesehatan Dunia, sekitar 1,9 miliar orang berusia 18 tahun ke atas mengalami kelebihan berat badan, dengan 600 juta orang mengalami obesitas. Menurut Survei Kesehatan dan Morbiditas Nasional, wanita 29,6% lebih mungkin mengalami obesitas dibandingkan pria, dibandingkan dengan 25% pria. Dataset rekam medis gagal jantung akan ditangani dalam dua tahap percobaan berdasarkan validasi. Empat algoritma klasifikasi yang berbeda, termasuk Random Forest, K-Nearest Neighbor, Decision Tree, dan Naive Bayes, akan dicoba pada langkah pertama. Untuk Testing, metode Cross Validation yang menggunakan Random Forest mengungguli empat algoritma lainnya dalam Testing algoritma. Setelah Testing, metode algoritma Random Forest menghasilkan nilai akurasi tertinggi, dan dievaluasi kembali menggunakan Split Validation dan rasio split yang bervariasi dengan Forward Selection sebagai fitu seleksi. Hanya Testing yang menggunakan metode Forward Selection mengungguli Testing yang menggunakan algoritma Random Forest.
Comparison of Phishing Detection Tests using the SVM Method with RBF and Linear Kernels Rumini, Rumini; Norhikmah, Norhikmah; Mustofa, Ali; Pradana, Sulistyo
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2882

Abstract

Phising adalah sebuah tindakan kriminal untuk mencuri informasi pribadi orang lain menggunakan entitas electronic, salah satunya adalah website. Informasi ini dicuri dari website yang telah diakses yang mengandung phising atau dengan kata lain masuk ke dalam kategori website phising. Tujuan dari web phising adalah membuat pengguna percaya bahwa mereka berinteraksi dengan situs resmi. Umumnya informasi yang dicari phisher (pelaku phising) adalah berupa username, password, baik itu akun media sosial atau akun nomor kartu kredit dengan cara diarahkan ke sebuah situs website palsu. Maka dari itu perlu adanya deteksi web phising yang berguna untuk melindungi user dari tindak pencurian informasi pengguna. Penelitian ini membahas dua kernel dalam metode SVM (Support Vector Machine) untuk deteksi web phising yaitu kernel RBF (Radial Basis Function) dan kernel linear. Akurasi yang didapatkan dengan ketiga kernel menghasilkan nilai akurasi yang berbeda-beda. Hasil akurasi pengujian sistem deketksi web phising dengan Kernel Linear sebesar 92.582 % dan Kernel Radial Basis Function sebesar 96.426 %. Akurasi paling tinggi dengan metode SVM untuk deteksi web phising yaitu menggunakan kernel RBF (Radial Basis Function).
Implementation of Deep Neural Network in the Design of Ethereum Blockchain Scam Token Detection Applications Pamungkas, Dimas Arya; Kharisma, Ivana Lucia; Simatupang, Dwi Sartika; Kamdan, Kamdan
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3162

Abstract

The popularity of blockchain continues to increase as technology develops, especially in the context of Ethereum as one of the leading blockchain platforms. However, this increase was also followed by many cases of fraud, especially in the form of tokens. In blockchain technology, tokens often refer to cryptocurrencies or digital currencies used as a means of exchange related to a particular project or platform. This research designs and builds an application system that can detect scam crypto tokens on the Ethereum blockchain, specifically for the ERC-20 (Ethereum Request for Comments 20) token type, which was proposed by Fabian Vogelsteller in November 2015, is a token standard that implements APIs for tokens. in Smart Contracts. Making a scam detection application implements the deep learning method with the Deep Neural Network (DNN) algorithm and evaluates performance using two test scenarios by dividing the dataset into three ratios of training data and test data. The output of the application is JSON-RPC which is integrated with the website. In testing the DNN model, using 80% training data and 20% test data, the DNN algorithm provides an accuracy of 0.997558%. Furthermore, system testing was carried out involving various scenarios to verify its functionality, including input validation, data extraction, DNN prediction, and display of prediction results, which gave good results from the system created. The application has succeeded in identifying scam tokens with high accuracy. , increasing user security in crypto transactions.
Analysis of Regency/City Human Development Index Data in East Java Through Grouping Using Hierarchical Agglomerative Clustering Method Alfirdausy, Roudlotul Jannah; Ulinnuha, Nurissaidah; Hafiyusholeh, Moh.
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2959

Abstract

The evaluation of human development is typically done using the Human Development Index (HDI), which measures the level of development in terms of various essential aspects of quality of life. In the case of East Java, the HDI is categorized as high. However. the distribution of HDI among the Regencies/Cities in East Java is still uneven. Therefore, it becomes necessary to cluster the districts/cities based on their HDI and the achievement of each indicator contributing to the HDI. Clustering is a data analysis technique used to group similar data together. Hierarchical agglomerative clustering is one of the methods used for this purpose. The aim of this study is to provide a reference for the government to understand the distribution of characteristic groupings among the districts/cities based on their HDI profiles in East Java. The analysis of East Java's HDI data for 2021 revealed that the best method and cluster was obtained using Average Linkage, with a Cophenetic coefficient value of 0.8105891, resulting in two clusters. The cluster with the highest Silhouette coefficient value of 0.6196077 comprised 34 districts/cities, classified as the low cluster, while the high cluster consisted of four cities/regencies.
Online Attendance with Python Face Recognition and Django Framework Dwi Rahmatya, Myrna; Wicaksono, Mochamad Fajar
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2773

Abstract

Online learning certainly requires an attendance system that is accessed anywhere with a minimum level of fraud. This research aimed to build an online attendance system using face recognition to prevent filling out online learning attendance represented by others. The online attendance system was built using the object-based system approach method. The system development method used was the waterfall. The development of this system utilizes the Django python framework, face recognition library, and OpenCV. This research delivered an attendance system that could not be represented by others. To record attendance, students visit the online attendance system. Students can only record attendance once according to the lecture schedule. The camera will capture the student’s face and equate it with the existing facial data. Only the registered student that his attendance data stored in the database. In addition, students cannot record attendance with face recognition outside of their lecture hours. This attendance system was tested using black-box testing. The test is carried out on the access button function to record attendance during the lecture schedules data and outside the lecture schedule, facial recognition function with valid and not valid facial data, function to store attendance data, and function to view attendance data recap in the current semester. The result showed that the attendance application with facial recognition is 100% running as it should and as expected.
Implementing Zero Trust Model for SSH Security with kerberos and OpenLDAP Mediana, Salwa Deta; lindawati, lindawati; Fadhli, Mohammad
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3330

Abstract

In order to remove trust presumptions towards the internal network, this study addresses the use of the Zero Trust Model in SSH (Secure Shell) security. The study approach is conducting tests by incorporating the Kerberos and OpenLDAP protocols into the SSH infrastructure. While OpenLDAP acts as a central directory for user management and permission access, Kerberos is utilized for single authentication and security resources like Kerberos tickets. As the server operating system for this investigation, Debian was used. Strong justification exists for securing SSH with Kerberos and OpenLDAP. SSH protocol assaults commonly target the standard port 22 (SSH), which is used for SSH. To ensure the security and integrity of the server system, the SSH port must be protected with Kerberos and OpenLDAP. SSH access is limited by Kerberos single authentication, which lowers the possibility of brute-force assaults and password theft. User administration and authorisation are facilitated by the integration of OpenLDAP. Implementing the Zero Trust strategy enables strong authentication and defends the system from insider threats. The system is protected from internal and external network assaults thanks to robust authentication, accurate authorisation, and isolating internal and external networks. An essential step in maintaining the security of the server system, data integrity, and information confidentiality is to secure port 22 and improve SSH with this integration. The research findings show that applying the Zero Trust model through this protocol integration greatly improves system security, resulting in better authentication and authorisation.
Chicken Disease Detection Based on Fases Image Using EfficientNetV2L Model Mustopa, Ali; Sasongko, Agung; Nawawi, Hendri Mahmud; Wildah, Siti Khotimatul; Agustiani, Sarifah
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2807

Abstract

Livestock farming requires technological innovation to increase productivity and efficiency. Chickens are a livestock animal with good market prospects. However, not all farmers understand about chicken diseases and signs of sickness. Detection of chicken diseases can be done through various methods, one of which is by looking at the shape of the chicken's feces. Images in feces can be detected using machine learning. Convolutional Neural Networks (CNN) are used to speed up disease prediction. Transfer learning is used to leverage knowledge that has been learned by previous models. In this study, we propose our own CNN architecture model and present research by building a new model to detect and classify diseases in chickens through their feces. The model training process is carried out by inputting training data and validation data, the number of epochs, and the created checkpointer object. The hyperparameter tuning stage is carried out to increase the accuracy rate of the model. The research is conducted by testing datasets obtained from the Kaggle repository which has images of coccidiosis, salmonella, Newcastle, and healthy feces. The results of the study show that our proposed model only achieves an accuracy rate of 93%, while the best accuracy rate in the study is achieved by using the EfficientNerV2L model with the RMSProp optimizer, which is 97%.
Comparison of Triple Exponential Smoothing and Support Vector Regression Algorithms in Predicting Drug Usage at Puskesmas Agnesti, Syafira; Nazir, Alwis; Iskandar, Iwan; Budianita, Elvia; Afrianty, Iis
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3499

Abstract

Drug management is important in managing adequate drug supplies in Puskesmas, to avoid errors in controlling existing drug stock inventory, it is necessary to predict the amount of drug usage by comparing Data Mining methods and Machine Learning methods, using the Triple Exponential Smoothing (TES) and Support Vector Regression (SVR) algorithms. Implementation is done using the Python programming language. The data used is Amlodipine 10 mg and Amoxicillin 500 mg drug data with a period of 42 months, from January 2020 - June 2023. This study aims to determine the best algorithm by comparing prediction error rate using the Mean Absolute Percentage Error (MAPE) method. Based on research that has been conducted on Amlodipine 10 mg and Amoxicillin 500 mg drugs with a division of 80% training data and 20% testing data, the Triple Exponential Smoothing algorithm with an additive model produces MAPE values of 10.36% and 17.50% respectively with the "Good" category. While Support Vector Regression algorithm, with RBF kernel, complexity 1.0, and epsilon 0.1 produces MAPE values of 10.31% and 9.38% in the "Good" and "Very Good" categories, respectively. Based on this, it can be concluded that Support Vector Regression algorithm is better at predicting than the Triple Exponential Smoothing algorithm.
Implementation of E-Govqual and IPA Models in Evaluating the Quality of Online Licensing System Services Pangestu, Danu Faisal; Wahyu, Sawali
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3149

Abstract

The online licensing information system website, hereinafter referred to as SIMPONIE, still faces a number of challenges as a transition from manual to digital system and has never been properly checked on the SIMPONIE website. This study aims to assess the good and bad level of SIMPONIE services using six e-govqual dimensions and provide advice based on the results of the Importance Performance Analysis (IPA) analysis. Research development on this method covers the wider scope of service quality. This study used a quantitative descriptive approach to collect data from 50 sample questionnaires and to carry out validity and reliability checks along with hypothesis testing and IPA quadrant analysis. According to the research results, 90.5% of the choices regarding service quality are influenced by factors of community support, function, and involvement as well as user convenience, reliability, and trust. With a gap value (GAP) of -0.75, there is a high priority scale for improvement in quadrant A, namely the license issuance function will be faster if it can be downloaded independently (RLB 2), the system often experiences errors or errors (FI 4) and live chat feature that needs improvement and human resource improvement. The implication of this research is that it can provide recommendations for improvements to improve the service quality of the online licensing system.

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