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All Journal Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) KLIK: Kajian Ilmiah Informatika dan Komputer
Fitri Insani
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Implementasi Data Mining Memprediksi Penjualan Crude Palm Oil Berdasarkan Kapasitas Tangki Menggunakan Multiple Linear Regression Ana Komaria Baskara; Alwis Nazir; Muhammad Irsyad; Yusra Yusra; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5665

Abstract

Data mining is a process of discovering information from data that can be used to improve business, product development, and other decision-making processes. One application of data mining is in PT. Kerry Sawit Indonesia, which is an agribusiness company in the Wilmar Group that deals with processing crude palm oil (CPO). Sales of CPO are crucial for palm oil plantation companies. To increase efficiency and profitability, palm oil plantation companies can predict CPO sales to optimize sales and CPO inventory. One method that can be used to predict CPO sales is through data mining techniques. In this study, the data mining technique used is multiple linear regression. Multiple linear regression is used to determine the relationship between the tank capacity variable and CPO sales. The data used in this study are CPO production data, CPO sales data, and tank capacity data obtained from palm oil plantation companies over the last five years. The results of the Multiple Linear Regression calculation in this case study show that the coefficient of determination (R-squared) value is 0.9546, indicating that 95.46% of the CPO delivery variability can be explained by the independent variables. Additionally, the MAPE and RMSE tests show that the regression model obtained has good accuracy in predicting CPO deliveries. Therefore, this regression model can be used to predict CPO deliveries in the future, considering the predetermined independent variable values.
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ancaman Resesi Ekonomi 2023 dengan Metode Naïve Bayes Classifier Dea Ropija Sari; Yusra Yusra; Muhammad Fikry; Febi Yanto; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 4 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6276

Abstract

Economic recession is a condition in which the economic turnover of a country changes to slow or bad that can last for years as a result of the growth of the Gross Domestic Product (GDP) a country decreases over two decades significantly. Early warnings of the emergence of a global recession become a concern for all countries in the world, even global recessions also have a major impact on Indonesia. Such as declining public spending due to decreasing incomes, increasing unemployment, increasing poverty, and many of whom have to accept PHK or salary cuts. Economic strengthening will be important in minimizing these threats, this research needs to be done to see the response of the public to the threat of economic recession. Twitter provides a container to users to comment on the problem of the economy recession 2023 which can be used as sentiment classification information to know positive and negative comments. This research uses the naive bayes classifier algorithm. In this study there are seven main processes, namely data collection, manual labelling, processing, feature weighing (tf-idf), tresholding, naive bayes method classification, testing. From the 1408 comments data on Twitter about the threat of a 2023 economic recession. Based on the results of the classification, using 2 testing models namely data balance and non-balance data obtained the best balance data test results with the highest accuracy result with the process of classification using algortima naïve bayes classifier resulted in accurateness of 78% obtainable by using a comparison of 90% training data and 10% test data.
Sistem Pakar Diagnosa Gangguan Stress Pasca Trauma Menggunakan Metode Certainty Factor Marliana Safitri; Fitri Insani; Novi Yanti; Lola Oktavia
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 4 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6309

Abstract

Mental health disorder or commonly called Mental Health Disorder is a disturbing psychological behavior and is followed by traumatic events such as shock shell, war fatigue, accidents, victims of sexual violence, and the covid pandemic. Cases of post traumatic stress disorder data from Indonesian Psychiatric Association amounted to 80% of 182 examiners experiencing symptoms of post-traumatic stress due to exposure to covid, 46% experienced severe symptoms, 33% moderate, 2% mild and others did not show symptom. This study aims to diagnose post traumatic stress disorder using the assurance factor method with 35 symptom data and 3 levels of post traumatic stress disorder as a knowledge base. The certainty factor is a circulation management method and a decision-making strategy using the confidence factor in the system. Based on the research results of the expert system for diagnosing post traumatic stress disorder, the test results obtained an accuracy of 80%. The results of the accuracy of this expert system indicate that the expert system can potentially be used to diagnose post traumatic stress disorder.
Estimasi Hasil Panen Ayam Pedaging Menggunakan Algoritma Regresi Linear Berganda Ahyani Junia Karlina; Muhammad Irsyad; Fitri Insani; Jasril; Eka Pandu Cynthia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.920

Abstract

Data mining is the process of collecting and managing information that aims to extract important data from data. Currently data mining is used by companies to manage data but there are still many companies engaged in the livestock sector that have not used data mining to manage data. One of these companies is PT.PX which is a broiler company located in Riau, precisely in Sungai Pagar. The ever-increasing need for broiler chickens makes it difficult for chicken breeders to produce chicken according to market demand in each period. Unpredictable demand for broiler chickens makes breeders confused to determine how many chicks to produce. PT.PX still manages data using Microsoft Excel so the process is still very long and it is not certain to get accurate results. PT.PX also does not have a system for predicting broiler yields to find out how many chicken populations there will be in the next period. The existence of this data mining can help breeders to find out the number of populations to be produced for the next period. In predicting broiler yields, estimation methods can be used using multiple linear regression algorithms. Multiple linear regression was used to determine the relationship between feed, weight and age of chickens and chicken population. The information used in this research is information on harvested chickens obtained from 2019 to 2022. The results of multiple linear regression calculations at PT.PX obtained broiler yields of 12,217 populations
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ancaman Resesi Ekonomi 2023 dengan Metode K-Nearest Neighbor Dimas Ferarizki; Yusra; Muhammad Fikry; Febi Yanto; Fitri Insani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1306

Abstract

A recession is a decline in overall economic activity, this is considered a phase of significant and sustainable economic decline in various sectors and economic indicators. The threat of a recession in 2023 has become a topic of discussion in many countries, including Indonesia. This happens because Indonesia is threatened as a country affected by a recession due to weakening economic activity in the real sector. This sentiment classification research aims to analyze public opinion and opinion regarding the issue of recession news in 2023 which is conveyed via the social media platform Twitter. This research aims to understand whether these opinions fall into the category of positive sentiment or negative sentiment. Apart from that, this research also aims to measure the level of accuracy in classifying these sentiments into appropriate classes. This research has several main processes starting from data collection then manual data labeling, text processing, feature weighting (TF-IDF), Thresholding feature selection and K-Nearest Neighbor method classification. Based on the classification results using a testing model from a total of 1000 comment data divided between 596 positive class data and 404 negative class Twitter data regarding the threat of recession in 2023, the highest accuracy results were obtained at 85% at a value of k = 3 using the 90:10 comparison model training and testing data
Perbandingan Klasifikasi Citra CT-Scan Kanker Paru-Paru Menggunakan Image Enhancement CLAHE Pada EfficientNet-B0 Dzaky Abdillah Salafy; Febi Yanto; Surya Agustian; Fitri Insani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1514

Abstract

In recent years, there has been a significant increase in the global cancer related mortality rate. Among various cancer types, lung cancer has emerged as one of the highest incidence cases. Lung cancer predominantly affects males and is attributed to several factors, including exposure to cigarette smoke, long-term air pollution, and exposure to carcinogenic compounds such as radon, asbestos, arsenic, coal tar, and diesel fuel emissions. The growth of cancerous cells in the lungs can be detected using various imaging techniques, with CT-Scan being one of them. This research focuses on the classification of normal lung organs and those affected by cancerous cells. The classification process employs two types of data: original data and data processed with Contrast Limited Adaptive Histogram Equalization (CLAHE). The data is initially divided with 90:10 ratios before being trained using a Convolutional Neural Network (CNN). The CNN architecture used is EfficientNet-B0, with the assistance of different optimizers and learning rates. After testing, the model's performance is evaluated using a confusion matrix to compare the results between the use of original data and CLAHE-processed data. The use of CLAHE processed data yields higher evaluation metrics compared to the original data, achieving a precision of 87.9%, recall of 85.6%, F1-score of 85.11%, and accuracy of 85.29% in the 90:10 data split, with the Adam optimizer and a learning rate of 10-1. The research results reveal that the utilization of image enhancement, specifically Contrast Limited Adaptive Histogram Equalization (CLAHE), with an appropriate combination of clip limit and tile grid, can impact the model's performance in classifying image data.
Prediksi Jumlah Perceraian Menggunakan Metode Support Vector Regression (SVR) Eka Suryani Indra Septiawati; Elvia Budianita; Fitri Insani; Lola Oktavia
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4613

Abstract

The increasing number of divorces poses an increasingly significant social challenge in Indonesia, including in the city of Pekanbaru. The impact of these divorces on the adolescent population can have negative effects on their emotional and psychological well-being, as well as their ability to interact socially and engage in the learning process. This study utilizes monthly divorce data from 2015 to April 2023 to conduct time series analysis and applies the Support Vector Regression (SVR) method to predict the number of divorces in the city of Pekanbaru. Three types of SVR kernels, namely linear, polynomial, and radial basis function (RBF), are evaluated and compared to find the kernel with the best Mean Squared Error (MSE) results. Through grid search analysis, optimal parameter values for each kernel are determined. The test results indicate that the SVR model with a polynomial kernel provides more accurate predictions with an MSE of 0.010228, compared to the linear kernel (MSE = 0.012767) and the RBF kernel (MSE = 0.010812).
Co-Authors Ahyani Junia Karlina Alwis Nazir Ana Komaria Baskara Dea Ropija Sari Dimas Ferarizki Dzaky Abdillah Salafy Eka Pandu Cynthia Eka Suryani Indra Septiawati Elvia Budianita Febi Yanto Jasril Jasril Lola Oktavia Marliana Safitri Muhammad Fikry Muhammad Irsyad Novi Yanti Surya Agustian Yusra Yusra Yusra