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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 40 Documents
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Search results for , issue "Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi" : 40 Documents clear
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.
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.
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.
A Robust Gender Recognition System using Convolutional Neural Network on Indonesian Speaker Switrayana, I Nyoman; Hadi, Sirojul; Sulistianingsih, Neny
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.v13i3.3698

Abstract

Voice is one of the biometrics that humans have. Humans can be recognized by the sounds produced by their vocal cords and vocal tracts. One of the uses of voice is to recognize gender. Despite extensive research, gender recognition using machine learning remains unsatisfactory due to the complexity of voice features and the limitations of conventional algorithms. In this research, voice-based gender recognition is performed by applying deep learning. The deep learning model used is the Convolutional Neural Network (CNN). The input of CNN is the result of feature extraction from the Mel-Frequency Cepstral Coefficients (MFCC) method. MFCC produces Mel-Spectograms which are important features of sound. The dataset used is Indonesian speech. In the research, there are imbalanced and balanced dataset scenarios to see the performance of the model. To produce a balanced dataset, random undersampling is performed on the majority class. In addition, the effect of dividing training and testing data with a composition of 70:30, 80:20, and 90:10 was observed. The results show that the model has 100% accuracy for all imbalanced dataset scenarios. Then the highest accuracy is 99.65% for the balanced dataset scenario with 70:30 splitting. In summary, it can be concluded that CNN performs very well in identifying gender from voice features overall, although its performance decreases when random undersampling is applied to the dataset.
Classification System for Soil Types Suitable for Food Crops using Naïve Bayes Method S.Kom., M.Kom (SCOPUS ID=ID: 57201646662), Nurdin
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.v13i3.3956

Abstract

Agriculture in Indonesia, especially in Aceh, plays a central role in supporting the economy and food security. The success of agriculture is greatly influenced by the selection of appropriate soil types. This research aims to develop a classification system for soil types suitable for food crops by applying the Naive Bayes algorithm to help farmers choose the right type of soil. The steps taken in this research are literature study, observation, interviews, data collection, system design and system implementation. In this study, the variables / criteria used include pH, humidity, drainage, soil texture, and nutrients, as input to provide recommendations for the most suitable soil type. By dividing the data into 70% training data and 30% testing data, the system achieved an accuracy rate of 83.3%. The results of the testing data used in this study were obtained in areas suitable for planting all three types of food, namely kong, tetinggi, Blangbengkik, Gantung Geluni, Porang Ayu, Anak Reje and Bener Baru. While the area that is only suitable for planting one type of food crop is Cinta Maju.
Application of User-Centered Design Method in E-Commerce Sales System Amanda, Retno Tri; Putri, Raissa Amanda
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.v13i3.4145

Abstract

E-commerce is the activity of buying and selling goods or services carried out electronically via the internet, involving online transaction, payment and delivery processes. The Azzahra Hijab Medan Shop is one of the shops engaged in selling hijabs in Medan City which still uses a conventional shop model without utilizing e-commerce to support the sales system. The transaction process in this shop is still done manually, from customers selecting goods to recording transactions in a book. Customers from outside the area can place online orders via the WhatsApp service with the shop owner providing a product photo catalog in stages. Therefore, an e-commerce system is needed to support online sales. The User Centered Design (UCD) method is important in designing e-commerce, starting with involving the user context in the early stages of development and involving system testing, enabling the creation of an optimal and user-friendly design. With the implementation of this E-Commerce based sales system, it is hoped that it will facilitate sales with more efficient transactions, faster data processing, help sales of goods become more accurate, and make it easier for consumers to order products.
Backpropagation Design for Authenticating Blood Vessel Patterns of the Back of the Hand Using GLRLM Syam, Fajar M; Yudono, Muchtar Ali Setyo; Sujjada, Alun
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.v13i3.4109

Abstract

Digital security is a critical aspect in the current era of information technology, where access to personal devices and data is often the main target by irresponsible parties. Traditional identification methods such as passwords and PINs are starting to show limitations in addressing increasingly complex security challenges.. The dorsal hand veins offer certain advantages that make them an attractive option for biometric recognition systems because the dorsal hand vein pattern tends to be stable over time, unaffected by external factors such as changes in weather or hygiene. This research aims to develop a system that can identify the blood vessels of the back of the hand as a biometric sign. The approach used involves extracting GLRLM features and applying the Back Propagation Neural Network identification method. The main goal is to achieve a higher level of accuracy than previous studies in the same domain. The identification process involves several stages, starting from image reception, image pre-processing, segmentation, feature extraction, identification, to obtaining images resulting from blood vessel identification. Test results show that the system developed achieved an average success rate of 82.52% based on five different test scenarios. The fourth scenario was proven to provide the highest test accuracy results, namely 87%.
A Web Scraper for Data Mining Purposes Mahmood, Yasir Ali; Mahmood, Bassim
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.v13i3.4107

Abstract

The current revolution in technology makes data a crucial part of real-life applications due to its importance in making decisions. In the era of big data and the massive expansion of data streams on Internet networks and platforms, the process of data collection, mining, and analysis has become a not easy matter. Therefore, the presence of auxiliary applications for data mining and gathering has become a necessary need. Usually, companies offer special APIs to collect data from particular destinations, which needs a high cost. Generally, there is a severe lack in the literature in providing approaches that offer flexible, low, or free of cost tools for web scraping. Hence, this article provides a free tool that can be used for data mining and data collection purposes from the web. Specifically, an efficient Google Scholar web scraper is introduced. The extracted data can be used for analysis purposes and making decisions about an issue of interest. The proposed scraper can also be modified for crawling web links and retrieving specific data from a particular website. It can also formalize the collected data as a ready dataset to be used in the analysis phase. The efficiency of the proposed scraper is tested in terms of the time consumption, accuracy, and quality of the data collected. The findings showed that the proposed approach is highly feasible for data collection and can be adopted by data analysts.
User Experience Testing on JoinGeek Admin using a User Experience Questionnaire and Usability Testing Kusuma Dewi, Fatimah Azzahra
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.v13i3.4084

Abstract

Job vacancy portals are becoming increasingly popular as a primary tool for job searching. As a result, developing and improving the quality of job vacancy portals is a suitable response to these changes. As job vacancy portals evolve, it is also vital to pay attention to adjustments to the dashboard that HR uses to process applications. As a technology firm, Geekgarden understands the importance of this development and is committed to improving JoinGeek Admin, their HR dashboard, to keep it relevant to this issue. This study compares the user experience on JoinGeek Admin before and after the redesign to determine the success of the new design. The methods employed are the User Experience Questionnaire (UEQ) and usability testing. The UEQ technique measures six dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The evaluation results reveal considerable improvements in all areas of the system before and after the redesign, which increase in each dimension: attractiveness (+2.166), perspicuity (+1.666), efficiency (+2.416), dependability (+2), stimulation (+2.5), and novelty (+0.834). Usability testing evaluates the system after redesign in terms of success rate, efficiency, and error rate. The test results reveal a success rate of 94%, an efficiency of 92.5%, and a low error rate of 3.8%. Thus, the evaluation findings show that the JoinGeek Admin redesign was successful in improving the user experience in all areas.

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