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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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Predict the thyroid abnormality particular disease likelihood of the symptoms’ certainty factor value and its confidence level: A regression model analysis Rosyid Ridlo Al-Hakim; Yanuar Zulardiansyah Arief; Agung Pangestu; Hexa Apriliana Hidayah; Aditia Putra Hamid; Aviasenna Andriand; Nur Fauzi Soelaiman; Machnun Arif; Mahmmoud Hussein Abdel Alrahman
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.v12i2.2542

Abstract

The traditional expert system (TES) in the medical field commonly uses a certainty factor (CF) rule-based algorithm that can be calculated several symptoms to determine the inference solutions. The main issue for this TES included a prediction for some particular disease likelihood in the cases of new patients. CF is calculated based on symptoms related to clinical signs in patients’ diagnoses. For some reason, this TES probably won’t predict uncertain things, such as particular disease likelihood of some diseases. So, supervised learning, such as linear regression, can solve this problem. We tried to analyse the existing TES for thyroid disorders due to modelling the regression equation to predict the thyroid abnormality particular disease likelihood, based on the symptoms’ CF value and its confidence level. We used multiple linear regression (MLR) and multiple polynomial regression (MPR) to analyse the best regression model to solve the problem. The results show that the MPR model indicates the best regression model for predicting particular disease likelihood of thyroid abnormality, supported by R-squared 94.7%, R-squared adjusted 94.4%, F-value 265.925, and p-value < 0.05, which are higher than MLR model. Our study proposed a foundation for expert system development by focusing more on machine learning expert system (MLES) analysis approaches than TES.
Network Forensic: Analysis of Client Attack and Quality of Service Measurement by ARP Poisoning using Network Forensic Generic Process (NFGP) Model Ramadhan, Rizdqi Akbar; Tira, Agro Tambas; Fadhilah, M. Rizki
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.v13i2.3804

Abstract

In computer network, communication from one computer to another computer can be intercepted, the way to intercept communication between network devices is with Address Resolution Protocol Poisoning attack. This attack can steal data such as usernames and passwords, modify traffic, and stop the traffic itself. This research implements the Network Forensic Generic Process model as a reference in Network Forensics practice. Apart from that, this research also measures quality of service to compare parameters before the attack and when the attack occurred. The tools used in this research are Wireshark, XArp, and Snort. This research succeeded in obtaining authentic information from the evidence obtained. The results of quality of service measurements showed that the quality of service parameters changed when the attack occurred. This research can be a reference in improving network security by better understanding the threats that may be encountered and providing valuable insight for future security prevention and response efforts.
Application of the Profile Matching Analysis Method in Decision Support Systems for Study Program Recommendations Rasyada, Reza Dian; Nurdin, Nurdin; Fajriana, Fajriana
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.v13i1.3161

Abstract

Currently, many prospective students are still confused about which study program to choose. One of the problematic factors is the lack of references from prospective students about the contents of each study program. This research aims to build a decision support system for study program recommendations using the Profile Matching Analysis method. The benefits of this research can provide final results in the form of recommended study program scores that are most suitable for prospective students. There are 5 assessment criteria used in this research, namely Language Values, Logic/IT Values, Science Values, Practice Values, and Social Values. The methods or stages carried out in this research are: data collection, system flowchart design, application of the Profile Matching Analysis method and system implementation. In this research, the recommendation results were obtained for a student with the name Afni Ruhmini based on the results of system calculations using the Profile Matching Analysis method, obtaining recommendation results for the Public Administration study program with a score = 5.3, the Marine Science study program with a score = 5.9, and the Agribusiness study program with a score = 5.6, the Physics Education study program with a score = 5.7 and the Law study program with a score = 4.8. The Profile Matching Analysis method is very suitable to be applied to solve problems in decision support system research for study program recommendations.
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%.
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.
Batu Pulut Village Population Management Information System using the Multi Attribute Utility Theory (MAUT) Method Siregar, Diana Asmarani; Samsudin, Samsudin
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.v13i1.3895

Abstract

Currently developing information technology can be used by village governments in order to increase the public reach of village governments for the community by providing easy access to information as well as developing better, transparent, effective and efficient village government activities. Currently, the population management information system in Batu Pulut village is still done manually or not many use media such as computers. The problems at the Batu Pulut Village Office are about work activities related to population service activities. The existing process is still in manual form, there is no system that is able to simplify the service process related to activities in the village and it still takes a long time so the process is often neglected. Another problem is in communities receiving aid where there is no system that is able to rank who the people are as recipients of village aid. The aim of this research is to create a population management information system for Batu Pulut Village using the Multi Attribute Utility Theory (MAUT) method. The method used in this system is the Multi Attribute Utility Theory method which is used as a method to assist this system in selecting communities as recipients of village assistance based on ranking data. The MAUT method aims to produce optimal decisions based on user assessments and references against a set of specified criteria. Based on the calculation results from the MAUT Method, a result of 0.75 was obtained with alternatives A2 and A4 as the best alternatives that were entitled to receive village assistance.
Genetic Grouping Algorithm based on Rank and Research Group for Timetabling Thesis Examination Habibie Ed Dien; M. Hasyim Ratsanjani; Andhika Satrio Wiratama; Vit Zuraida
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.v12i2.2394

Abstract

Timetabling is a common problem faced by various academic institutions, especially in the process of timetabling thesis examination. In the process of timetabling thesis examination there are several problems that arise, namely the problem of determining examiners and their order, arranging space and time slots, which makes the timetabling process inefficient. This problem will be solved by using a genetic grouping algorithm (GGA) combined with the parameters of rank and research groups (RG), which have proven to be efficient to use to solve problems such as in the thesis exam timetabling process. The results showed that GGA with ran and RG succeeded in increasing efficiency by 99,97% when applied to solving this problem in the thesis exam timetabling process.
E-Posyandu to Improve Maternal and Child Health Services in Desa Aek Nagali Merilan, Fina; Helmiah, Fauriatun; Nehe, Nurkarim
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.v13i2.3948

Abstract

Posyandu's role is to monitor the growth and development of children, detect diseases from an early age, and monitor the health of mothers and children in order to realize the social welfare of the community. With the internet as a medium for distributing information and services more effectively and efficiently, Posyandu can provide better information and services to the public through a website. Posyandu Aek Nagali Village does not yet have an information technology-based application that helps Cadres to record and find back the information handled by mothers and children. All records are still done manually on paper. Of course this is very vulnerable to data loss, data recording errors, difficulty finding data and unable to access data together. Recapitulation that is done manually is very prone to errors and will take up quite a lot of time. This manual recording process also complicates the reporting process to the village level. Likewise, when cadres report results to the Puskesmas, they still have to do manual recapitulation. This information is useful for knowing the child's growth, whether there are deviations or not. If there is a deviation, it is immediately known and followed up by medical personnel at Posyandu.
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.
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.

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