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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 1,011 Documents
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Measuring IT Governance Capability at DISKOMINFO Salatiga using COBIT 2019 Parera, Neonatal March; Tambotoh, Johan Jimmy Charter
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): 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.v13i1.3669

Abstract

The rapid development of Information Technology has become a key driver in supporting various sectors and organizational objectives. In the past, information technology was perceived as a support system, but it is now recognized for its significant benefits. Therefore, Information Technology Governance (ITG) is essential to integrate information technology into the organization's strategy. This study aims to measure IT governance capability and provide improvement recommendations using the COBIT 2019 framework to support the vision and mission of DISKOMINFO Salatiga, which is to enhance the quality of public service and achieve good governance The research method employed is qualitative, utilizing the COBIT 2019 framework, and data collection techniques involve observation, interviews, and questionnaires. The results indicate several domains that need enhancement, such as APO12, BAI03, BAI06, BAI07, and BAI10. The identified gap between expected and current capabilities necessitates improvement recommendations, including risk management, system monitoring, structured IT change, project evaluation, and periodic configuration verification. By implementing these recommendations, it is expected that DISKOMINFO Salatiga can achieve the desired IT governance capability, thereby supporting the achievement of Good Governance in line with the organization's vision and mission
Mobile-based Electronic Health Card Application using Realtime Push Notification Husna, Fauziatun; Suendri, Suendri
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): 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.v13i3.4146

Abstract

The Healthy Way Card (KMS) is a valuable tool for documenting and tracking the growth and development of children between the ages of 0 and 5 years. The aim of this research is to make it easier for cadres and parents to see the development of toddlers as well as reminders of posyandu activity schedules in real time. This research uses Firebase Realtime Database for storing toddler data and Firebase Storage for storing media/photos. Use of Java and Android Studio as programming languages. The Waterfall system development method is also used. Push Notification is divided into 2 privileges, namely Broadcast for routine posyandu information which is sent to all users and Private which is pushed to each user's account for the development of toddlers after activities. The Towards Healthy Card Application System has a multiplatform, namely Mobile and Web. The system used can run well. The system can be accessed by several users so that it can be used to record data on toddler development, as well as obtain information on posyandu activities in real time. Thus, from the test results, the system can display growth and development graphs, immunization graphs, weight graphs, from all these graphs you can see toddler development data.
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.
Predicting Potential Car Buyers using Logistic Regression Algorithm Lapatta, Nouval Trezandy; Husin, Abdullah
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): 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.v13i3.4068

Abstract

This research aims to develop a predictive model to identify individuals with a high potential to become car buyers, employing logistic regression algorithm. The primary objective is to support the automotive industry in devising more efficient and focused marketing strategies. The choice of logistic regression is based on its superiority in handling categorical dependent variables and its practicality in result interpretation. The data processed in this study derive from demographic information, consumption habits, brand preferences, and various other factors that influence car buying decisions. The main data source is the outcome of online surveys participated in by individuals predicted to have the potential to buy a car within the next 12 months. The analysis results indicate that factors such as income, age, previous vehicle ownership status, gender and marriage status play significant roles in predicting the likelihood of someone becoming a car buyer. The developed model achieved an accuracy and precision of 95%, proving its significant capability in identifying potential car buyers with a high success rate. These findings provide valuable insights for the automotive industry in formulating more targeted and efficient marketing strategies, as well as contributing to the academic literature on the application of logistic regression in consumer behavior prediction.
Identification of Lumpy Skin Disease in Cattle with Image Classification using the Convolutional Neural Network Method Sentoso, Thedjo; Ardiansyah, Fachri; Tamuntuan, Virginia; Wangsa, Sabda Sastra; Kusrini, Kusrini; Kusnawi, Kusnawi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): 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.v13i3.2569

Abstract

One of the problems often faced by cattle farmers is related to diseases in their cattle where one of the cattle diseases whose transmission rate is very fast is Lumpy Skin Disease (LSD). Currently, to identify the health of livestock, especially in cattle, is still very dependent on experts and of course this takes time, resulting in delays in the prevention and treatment of diseases in cattle, especially this LSD disease. The Convolutional Neural Network (CNN) algorithm is one of the algorithms can used for image classification of cows whether the cow is healthy or Lumpy. The stages of this research start from problem identification, literature study, data collection, algorithm implementation, testing, and performance evaluation results of the algorithm on cattle disease data. In this research, testing was conducted using three architectures for CNN: VGG16, VGG19, and ResNet50. The results of the experiment showed that VGG16 was the most effective architecture compared to VGG19 and ResNet50, with a training accuracy of 95.31% and a loss value of 0.1292, as well as a testing accuracy of 96.88% and a loss value of 0.102.
Design and Implementation of an Android based New Student Admissions Classification System using the Vikor Method Wahyu Soataon Hasibuan; Heri Santoso
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (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.v12i2.2838

Abstract

Today's technological developments make all aspects of life easier. One form of convenience that can be found in the world of lectures is a digital student graduation information system. In this study the authors designed an android-based new student admission classification system using the vikor method. In simple terms, the vikor method can be used to perform multicriteria ranking and weighting of each assessment criterion. In the implementation of the North Sumatra State Islamic University Independent Examination, there are several materials to be tested such as Religious Sciences, General Sciences, Arabic, English, and Interviews where the system still uses a passing grade where its application is considered less efficient. The use of the vikor method in this study is intended to carry out the process of ranking student grades based on predetermined weights based on the criteria of several materials tested. In this study, the creation of an android application using the app inventor platform. The results in this study indicate that the vikor method can be implemented in the new student admission classification system and perform ranking based on the exam results that have been carried out using an android smartphone.
Chili Leaf Health Classification using Xception Pretrained Model Wulandari, Yestika Dian; Munggaran, Lulu Chaerani; Setiawan, Foni Agus; Satya, Ika Atman
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): 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.v13i3.3943

Abstract

As one of the high-demand horticultural crops, chili peppers have a significant impact on the economy of Indonesia. However, despite the growing demand and interest in chili peppers, their production often faces disruptions due to crop failures. One of the leading causes of such failures is pests and diseases. Among all parts of the chili plant, chili leaves are the most susceptible to damage. Distinguishing between healthy and unhealthy chili leaves can serve as an early detection step for chili diseases and preventive measures to contain their spread. Convolutional Neural Network (CNN) are effective algorithms for image classification. The development of CNN has led to the use of models previously trained on large datasets to accurately classify relatively small datasets. One such pretrained model known for its exceptional classification capabilities is Xception. By utilizing the pretrained Xception model trained on the ImageNet dataset for the classification of healthy and unhealthy chili leaf images, our model achieved an accuracy of 91% on a dataset containing 2136 images. Furthermore, the model achieved a 100% success rate by correctly predicting all 10 out of 10 given images.
Design of GeoAi-Based Control Tower Dashboard Application Infrastructure at PT. XYZ Siregar, Maulana Bobby Rakhman; Aji, Rizal Fathoni
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): 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.v13i1.3466

Abstract

The increasing demand for technology and data necessitates the enhancement of infrastructure for the Control Tower Dashboard Geographic Information System (CTD GIS) application at PT XYZ. The current system operates within a Virtual Private Server (VPS) Cloud environment but faces challenges such as data loading delays and increasing demands for broader functionalities. To address these issues, a comprehensive future infrastructure recommendation is outlined, including upgrading ArcGIS Server, integrating with Portal for ArcGIS, implementing ArcGIS Datastore, utilizing NAS Storage, and incorporating Script & VGA Server. Through the proposed infrastructure changes, the CTD GIS application is poised to navigate the dynamics of data growth, providing geospatial insights to support better decision-making processes at PT XYZ.
Development of an Agriculture Mobile Learning System using the Peer-To-Peer (P2P) Model Delima, Rosa; Wibowo, Argo
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): 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.v13i3.3489

Abstract

E-learning is a learning process that utilizes an internet medium that connects teachers and students using multimedia-based teaching materials. M-learning is a learning model that has many similarities with e-learning; only the M-learning interaction model between teachers, students, and teaching materials is facilitated by mobile devices. Currently, quite a lot of mobile learning is being developed in Indonesia, but most of the research focuses on students. Not many have developed M-learning for the general people with the specific content or teaching materials in agriculture. An agriculture learning system is developed by applying the pear-to-pear (P2P) model between teachers and participants. The system applies two platforms, namely web and mobile. Admins and teachers use the web application, while the mobile application is used by the participants. Based on the system testing results, it is known that all functions in the system have been running well. Evaluation of the system's suitability with user requirements also shows that the system has accommodated all requirements.
Classification of Suspicious Financial Transactions using Light Gradient Boosting Machine Method (LGBM) based on Social Network Analysis (SNA) Indicators Ayu Fara Paramitha; Yuti Dewita Arimbi; Slamet Riyanto; Niken Fitria Apriani; Al Hafiz Akbar Maulana Siagian
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): 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.v13i2.3273

Abstract

Money laundering is an act committed by individuals or a group to conceal or disguise the origin of wealth obtained from illegal activities into assets that appear to have been acquired through legal means. Generally, there are three money laundering processes: placement, layering, and integration. The complexity of these money laundering processes described above makes it difficult to trace suspicious financial transactions and identify the parties involved and which transactions are connected to the suspected money laundering network. To address this issue, Social Network Analysis (SNA) is implemented to generate SNA features. In the following stage, these SNA features are employed as indicators to detect suspicious financial activities. The gathered indicator data is utilized to build a classification model using the Light Gradient-Boosting Machine (LGBM) approach. The results of this study show that the model created using SNA and LGBM methods achieved an accuracy of 97%. The precision, recall, and F1-Score values for non-suspicious transaction data were 98%, 97%, and 97%, respectively, while for suspicious transaction data, they were 97%, 98%, and 97%, respectively. The achieved accuracy values were quite high indicating that the used approach was capable of effectively classifying suspicious financial activities. We believe that the findings of this study could be an alternative method for detecting suspicious financial transactions in order to avoid money laundering operations.

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