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Scientific Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : 24077658     EISSN : 24600040     DOI : -
Core Subject : Science,
Computer Science & IT
Scientific Journal of Informatics published by the Department of Computer Science, Semarang State University, a scientific journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
Arjuna Subject : -
Articles 7 Documents
 1 
Search results for , issue "Vol 10, No 1 (2023): February 2023" : 7 Documents clear
Software Effort Estimation Using Logarithmic Fuzzy Preference Programming and Least Squares Support Vector Machines Adnan Purwanto; Lindung Parningotan Manik
Scientific Journal of Informatics Vol 10, No 1 (2023): February 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i1.39865

Abstract

Purpose: Effort Estimation is a process by which one can predict the development time and cost to develop a software process or product. Many approaches have been tried to predict this probabilistic process accurately, but no single technique has been consistently successful. There have been many studies on software effort estimation using Fuzzy or Machine Learning. For this reason, this study aims to combine Fuzzy and Machine Learning and get better results.Methods: Various methods and combinations have been carried out in previous research, this research tries to combine Fuzzy and Machine Learning methods, namely Logarithmic Fuzzy Preference Programming (LFPP) and Least Squares Support Vector Machines Machine (LSSVM). LFPP is used to recalculate the cost driver weights and generate Effort Adjustment Point (EAP). The EAP and Lines of Code values are then entered as input for LSSVM. The output results are then measured using the Mean Magnitude of Relative Error (MMRE) and Root-Mean-Square Error (RMSE). In this study, COCOMO and NASA datasets were used.Result: The results obtained are MMRE of 0.015019 and RMSE of 1.703092 on the COCOMO dataset, while on the NASA dataset the results of MMRE are 0.007324 and RMSE are 6.037986. Then 100% of the prediction results meet the 1% range of actual effort on the COCOMO dataset, while on the NASA dataset, the results show that 89,475 meet the 1% range of actual effort and 100% meet the 5% range of actual effort. The results of this study also show a better level of accuracy than using the COCOMO Intermediate method.Novelty: This study uses a combination of LFPP and LSSVM, which is an improvement from previous studies that used a combination of FAHP and LSSVM. The method used is also different where LFPP produces better output than FAHP and all data in the dataset is used for training and testing, whereas in previous research it only used a small part of the data.
Sentiment Analysis on Social Media Against Public Policy Using Multinomial Naive Bayes Wildan Budiawan Zulfikar; Aldy Rialdy Atmadja; Satrya Fajri Pratama
Scientific Journal of Informatics Vol 10, No 1 (2023): February 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i1.39952

Abstract

Purpose:  The purpose of this study is to analyze text documents from Twitter about public policies in handling COVID-19 that are currently or have been determined. The text documents are classified into positive and negative sentiments by using Multinomial Naive Bayes.Methods:  In this research, CRISP-DM is used as a method for conducting sentiment analysis, starting from the business understanding process, data understanding, data preparation, modeling, and evaluation. Multinomial Naive Bayes has been applied in building classification based on text documents. The results of this study made a model that can be used in classifying texts with maximum accuracy.Result:  The results of this research are focused on the model or pattern generated by the Multinomial Naive Bayes Algorithm. The classification results of social media users' tweets against the new normal policy obtained good results with an accuracy value of 90.25%. After classifying the tweets of social media users regarding the new normal policy, the results show that more than 70% agreed and supported the new normal policy.Novelty:  This study resulted in how classification can be done with Multinomial Naive Bayes and this algorithm can work well in recognizing text sentiments that generate positive or negative opinions regarding public policies handling COVID-19. So, the research provided conclusions about the views of people around the world on new normal public policy.
Monthly Rainfall Prediction Using the Backpropagation Neural Network (BPNN) Algorithm in Maros Regency Muhammad Arief Fitrah Istiyanto Aslim; Jasruddin Jasruddin; Pariabti Palloan; Helmi Helmi; Muhammad Arsyad; Hari Triwibowo
Scientific Journal of Informatics Vol 10, No 1 (2023): February 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i1.37982

Abstract

Purpose: This study aims to identify the right combination of network architecture, learning rate, and epoch in making predictions at each rainfall post in Maros Regency. In addition, this study also predicts the monthly rainfall profile in 2021-2025 in Maros Regency.Methods: The method in this study is the backpropagation neural network algorithm to learn and predict the data. BPNN is one of the most commonly used non-linear methods in making predictions recently. The data used in this study is monthly rainfall data from 2000-2020 as training and testing data at four rainfall stations including BPP Batubassi, Staklim Maros, Stamet Hasanuddin, and BPP Tanralili.Result: The results showed that the combination of network architecture, learning rate, and epoch obtained at each rainfall post was different. The highest level of prediction accuracy was obtained on 5 layers rather than 3 or 4 layers of network architecture with prediction accuracy at each rainfall post respectively 76.91%, 72.47%, 75.24%, and 76.53%. The predictions of rainfall from 2021-2025 are following the monsoon rain pattern with the highest rainfall in January 2025 of 964.1 mm, while the largest annual rainfall is obtained in 2023 with a total of 3359.6 mm.Novelty: In this study, various combinations of network architecture parameters consisting of learning rate, epoch, and architecture at each rainfall post obtained different results. Particularly in the Maros Regency, the combination that is most suitable for use in predicting monthly rainfall at the Batubassi BPP post is learning rate 0.7, epoch 50000, and network architecture 11-6-10-7-5, at Staklim Maros post is learning rate 0.5, epoch 50000, and network architecture 11-5-9-10-5, at Stamet Hasanuddin post is learning rate 0.8, epoch 20000, and network architecture 11-5-8-6-5, and at BPP Tanralili post is learning rate 0.5, epoch 10000, and 11-5-9-9-5 network architecture.
Simulation Study of Imbalanced Classification on High-Dimensional Gene Expression Data Masithoh Yessi Rochayani; Umu Sa'adah; Ani Budi Astuti
Scientific Journal of Informatics Vol 10, No 1 (2023): February 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i1.40589

Abstract

Purpose: Classification of gene expression helps study disease. However, it faces two obstacles: an imbalanced class and a high dimension. The motivation of this study is to examine the effectiveness of undersampling before feature selection on high-dimensional data with imbalanced classes.Methods: Least Absolute Shrinkage and Selection Operator (Lasso), which can select features, can handle high-dimensional data modeling. Random undersampling (RUS) can be used to deal with imbalanced classes. The Classification and Decision Tree (CART) algorithm is used to construct a classification model because it can produce an interpretable model. Thirty simulated datasets with varying imbalance ratios are used to test the proposed approaches, which are Lasso-CART and RUS-Lasso-CART. The simulated data are generated from parameters of real gene expression data.Results: The simulation study results show that when the minority class accounts for more than 25% of the observation size, the Lasso-CART method is appropriate. Meanwhile, RUS-Lasso-CART is effective when the minority class size is at least 20 observations.Novelty: The novelty of this simulation study is using the RUS-Lasso-CART hybrid method to address the classification problem of high-dimensional gene expression data with imbalanced classes.
Developing a Digital Scales System using Internet of Things Technology on Indonesia Digital Farm Kusrini Kusrini; Banu Santoso; Eko Pramono; Muhammad Koprawi; Jeki Kuswanto; Elik Hari Muktafin; Ichsan Wasiso
Scientific Journal of Informatics Vol 10, No 1 (2023): February 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i1.40956

Abstract

Purpose: This research aims to develop a digital scales system using internet of things technology on Indonesia digital farm.Methods: The stages of the research were carried out starting from literature studies, system requirements analysis, digital scales system design, system testing, and analysis of system test results. This model consists of hardware and software. The hardware consists of sensors for data collection in the field or using cameras, data input devices, data senders to data centers, data centers, and data processors, and data output that can be accessed on a laptop or a smartphone in real-time.Result: The results of the study show that IoT-based digital scales can be used to read goat weighing results based on RFID data input and camera image capture. The average body weight of a goat that has been weighed is 106.5 pounds, while the average body height of a goat is 150.7 cm.Novelty: The IoT-based digital scales system (IoT-DSS) can be used to measure the weight and height of goats so that the weighing process is more efficient.
Hybrid Water Feedback Solutions Using Internet of Thing (IoT) Enabled Water Pumps Powered by Solar Panels Achmad Buchori; Adhi Kusmantoro; Aan Burhanuddin; Takashi Hiyama
Scientific Journal of Informatics Vol 10, No 1 (2023): February 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i1.41007

Abstract

Purpose: Renewable energy in Indonesia is very abundant, one of which is solar energy. With Indonesia's location on the equator, the abundant potential of solar energy can be utilized as an environmentally friendly source of electricity. To increase the potential of solar energy as a source of electricity, it is equipped with a battery storage system. The most widely used electrical energy storage system is the battery. The purpose of this study is the use of solar energy for DC water pumps. The proposed system is also equipped with power, voltage and current monitoring on the solar panel side and on the load side (water pump).Methods/Study design/approach: The method used is to conduct a literature review to study and find out the development of a power monitoring system using solar panels. The next step is to measure solar radiation, calculate PV capacity and SCC. Furthermore, designing, modeling, simulating, analyzing, and implementing the optimal topology for water pump control using solar panels.Result/Findings: The results of the research are water pump control coordination devices using Sonoff with an IoT-based monitoring system. This device is capable of controlling PV and battery power flow. A prototype of a solar water pump has been produced which has been validated by experts in the field of appropriate technology with feasible results and received a positive response from farmers in Demak to be immediately implemented in the fields to help with the water crisis in agricultural landNovelty/Originality/Value: the advantage of this solar water pump is that the product is equipped with the internet of things (IoT) which can control the use of water pumps in the fields with our android devices wherever we are, this makes it easy for farmers to apply them, then the water pumps also do not use electricity which makes this water pump not harmful to farmers, because in the past many farmers were electrocuted and died.
Stacking Ensemble Learning to Improve Prediction Accuracy in P2P Lending Platform
Scientific Journal of Informatics Vol 10, No 1 (2023): February 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i1.42453

Abstract

Abstract. Purpose: The purpose of this study is to improve accuracy on the prediction of default risk. Defaults on p2p lending platforms result in significant losses for lenders and pose a threat to the efficiency of the overall peer-to-peer lending system. It is then very important to have an understanding of the methods capable of managing such risks Even with only a slight improvement in the accuracy of the model's ability to anticipate default significant losses can be avoided.Methods: The method used to make predictions is a combination method of stacking ensemble models with the LightGBM metalearner as the final estimator. While the base-learner algorithms used are Multi Layer Perceptron (MLP), Support Vector Machine (SVM) and Random Forest (RF).Result: The PPDai dataset used in this study is P2P lending data from the PPDai platform from China. The data split process uses the 10-fold cross validation method. Evaluate the model using a confusion matrix that generates accuracy values. The evaluation results showed that Stacking-LightGBM as the best model in the PPDai dataset received an accuracy of 87.18%.Novelty: This research shows that the accuracy of the peer-to-peer lending default prediction model  can be improved using the Stacking-LightGBM method. The stacking ensemble method can beat the accuracy of a single classification algorithm in terms of making predictions.  The use of meta-learners can improve the performance of ensemble stacking models.

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