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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Media Statistika Jurnal Studi Manajemen Organisasi Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah KOMPUTASI BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Jiko (Jurnal Informatika dan komputer) JURNAL PENDIDIKAN TAMBUSAI JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Pendidikan dan Konseling JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) International Journal of Advances in Data and Information Systems Al-Mutharahah: Jurnal Penelitian dan Kajian Sosial Keagamaan Studies in Learning and Teaching Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Jurnal Bisnis Indonesia International Journal of Community Service International Journal of Data Science, Engineering, and Analytics (IJDASEA) Al Khidma: Jurnal Pengabdian Masyarakat Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Inkofar Kreatifitas: Jurnal Ilmiah Pendidikan Islam Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Jurnal Ilmiah Edutic : Pendidikan dan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Eksponensial Baitul Hikmah: Jurnal Ilmiah Keislaman STATISTIKA Kohesi: Jurnal Sains dan Teknologi ITIJ Seminar Nasional Teknologi dan Multidisiplin Ilmu Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal ilmiah teknologi informasi Asia RAGAM: Journal of Statistics and Its Application Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Trimono, Trimono
Data Science Department, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Sentiment Analysis on Digital Korlantas POLRI Application Reviews Using the Distilbert Model Putri, Nabila Rizky Amalia; Trimono, Trimono; Damaliana, Aviolla Terza
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 4 No. 2 (2024): September 2024
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v4i2.17197

Abstract

The implementation of digitalization in public services by Korlantas Polri has facilitated faster administration, wider access, and improved service quality. The Korlantas Polri Digital app has garnered more than 5 million downloads on the Google Play Store, with a rating of 3.7 and around 110 thousand reviews. Given that an app's reputation can be significantly affected by criticism, sentiment analysis becomes very important to categorize user reviews as positive, negative, or neutral, thus assisting developers in identifying app shortcomings. This study uses DistilBERT, a deep learning model distilled from BERT, to assess the effectiveness of sentiment analysis on reviews. Data was collected from user reviews on the Google Play Store between September 1, 2023 and May 31, 2024, resulting in 8,752 reviews retained for analysis. Model performance was evaluated at three data ratios: 60:40, 70:30, and 80:20, with the best performance results seen at a ratio of 80:20, achieving 88% accuracy. Increasing the training data ratio from 60:20 to 80:20 has a positive impact on the model, suggesting that the model can learn better with larger training data.
Modelling of Return of S&P 500 Using the Non Linear Generalized Autoregressive Conditional Heteroscedasticity (NGARCH) Model Trimono Trimono; Aviolla Terza Damaliana; Irma Amanda Putri
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4110

Abstract

ARIMA Box-Jenkins is one of the most popular forecasting methods. ARIMA modeling requires a non-heteroskedastic care that shows constant residual variants. Awake, meaning residual residue from heteroscedastic ARIMA modeling (not constant). To overcome the problem of residual heteroskedasticity ARIMA used modeling volatility that is Generalized Autoregressive Conditional Heteroscedasticity (GARCH). GARCH is used to model the ARIMA residual variant which means symmetric. Some financial data has an asymmetric nature caused by the influence of good news and bad news. To accommodate these asymmetric properties, we use the Non-Linear Generalized Autoregressive Conditional Heteroscedasticity (NGARCH) volatility model which is the development of the GARCH model. This research applies NGARCH model using S & P 500 share price index data from January 1, 2019, until July 31, 2023 during active day (Monday-Friday). The purpose of this study, to find the best model NGARCH. The best model generated for S & P 500 stock price index data is ARIMA (1,0,1) NGARCH (1,1) because it has small AIC.
Forecasting The Number of Traffic Accidents in Purbalingga Regency on 2023 Using Time Series Model Trimono; Amri Muhaimin; Nabilah Selayanti
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4168

Abstract

Accident data from Satlantas Purbalingga Regency shows that in 2022 there is an increase in the number of traffic accidents in the Purbalingga Regency. In the future, the impact of accidents is predicted to be bigger so it is necessary to forecasting. Forecasting is one of the most important elements in decision making, because effective or not a decision generally depends on several factors that can not be seen at the time the decision was taken. In this time study the possible time series model is ARMA (2,2), ARMA (2,1), ARMA (1,2), ARMA (1,1), AR (2), AR (1), MA (2), MA (1). However, after testing, the model used is ARMA (1,1). This model is used because it meets all the assumption requirements that are parameter significant, residual independent test, residual normality test, and the smallest Mean Square Error value. According to data forecasting results the highest number of crashes existed in January of 97 accidents and the lowest in December amounted to 93 accidents, So the necessary action from the relevant agencies to cope with the increasing number of traffic accidents in the Purbalingga Regency.
Stock Price Modeling with Geometric Brownian Motion and Value with Risk PT Ciputra Development TBK Amri Muhaimin; Trimono Trimono
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3329

Abstract

Financial sector investment is an activity that attracts a lot of public interest. One of them is investing funds in purchasing the company’s shares. Profit received from stock investment activity can be seen from the value of stock returns. While, if the previous stock returns to Normal distribution, the future stock price can be predicted by Geometric Brownian Motion Method. Based on the stock price prediction, can also be measured an estimated value of the investment risk. The result of data processing shows that the stock price prediction of PT. Ciputra Development Tbk period December 1, 2016, until January 31, 2017, has very good accuracy, based on the value of MAPE 1.98191%. Further, the Value Risk Method of Monte Carlo Simulation with ? = 5% significance level was used to measure the share investment risk of PT.Ciputra Development Tbk. Thus, this method is only useful if it can be used to predict accurately. Therefore, backtesting is needed. Based on the processing obtained data, backtesting generates the value of violation ratio at 0, it means that at significance level ? = 5%, the Value at Risk Method of Monte Carlo Simulation can be used at all levels of probability violation.
Application of Google Data Studio for Data Visualization at SMK Tunas Bangsa Malang Trimono; Andreas Nugroho Sihananto; Muhammad Muharrom Al Haromainy; Edi Sugiyanto; Farkhan
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.33107

Abstract

The Department of Office Automation and Governance (OTKP) is one of the Vocational High School’s majors in Indonesia that focuses on office operations and information processing. One of the popular skill in information processing lately is data processing and visualization. In response of this trend, we propose a Google Data Studio training for Tunas Bangsa Vocational High School’s students from OTKP Majors. Google Data Studio is a free data analysis tool from Google. With this tool, users can not only display data with attractive and easy-to-understand visuals but also can process data from various sources on one worksheet. This service is mostly free, not limited to Google services such as Google Sheets but can be linked to other platforms, such as websites, applications or third party services. By the end of the training all participants have been able to use Google Data Studio for data visualization needed for offices in general.
COMPARISON OF DECISION TREE AND RANDOM FOREST METHODS IN THE CLASSIFICATION OF DIABETES MELLITUS Maulidiyyah, Nova Auliyatul; Trimono, Trimono; Damaliana, Aviolla Terza; Prasetya, Dwi Arman
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8316

Abstract

Diabetes mellitus is a deadly disease caused by the failure of the pancreas to produce enough insulin. Indonesia ranks fifth in the world with the number of people with diabetes in 2021 at around 19.47 million, and this number continues to increase. One of the main challenges in diabetes management is to make the right classification between type 1 and type 2 diabetes, as misdiagnosis can result in inappropriate treatment and worsen the patient's condition. This study uses a machine learning approach to compare Decision Tree and Random Forest methods in classifying type 1 and type 2 diabetes mellitus. The goal is to identify the most effective model in predicting the type of diabetes based on medical record data. The comparison was done using k-fold cross validation and confusion matrix. The results showed that Random Forest provided an average accuracy of 94%, while Decision Tree reached 93% during cross validation testing. Although both models were able to perform well in classification, Random Forest showed a more stable performance and a slight edge in accuracy over Decision Tree. Evaluation with the confusion matrix showed that the Decision Tree model achieved 93% accuracy compared to Random Forest's 91%. In addition, the Decision Tree model also had a lower number of prediction errors, 7, compared to 9 for Random Forest. The most influential variables in classification also differed between the two models, showing the unique advantages and characteristics of each approach.
ANALYSIS OF CLUSTERING METHODS ON THE CAUSAL FACTORS OF DIABETES MELLITUS WITH FUZZY C MEANS METHOD Adiwidyatma, Afdhal Reshanda; Mas Diyasa, I Gede Susrama; Trimono, Trimono
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i2.611

Abstract

This study focuses on the effectiveness of clustering algorithms, namely Fuzzy C-Means by using k-Means algorithm as a supporting method, in the factors that cause Diabetes Mellitus. Diabetes mellitus is a chronic disease characterized by high levels of sugar (glucose) in the blood. Indonesia ranks 5th with the highest diabetes Mellitus patients in the world. This study aims to understand the pattern of factors causing Diabetes Mellitus and test the effectiveness of the clustering algorithm used. The data analysis methods include data collection, data pre-processing, distribution of cluster numbers, algorithm implementation, model adjustment, model training, model evaluation, and analysis of results. The results showed that the Fuzzy C-Means algorithm gets a coefficient of Fuzzynes score of 0.23 with a validation score of 0.40, while for supporting methods used K-Means algorithm gets a validation score of 0.32. This result shows that Fuzzy C-Means algorithm is superior in clastering the factors that cause Diabetes mellitus. The results of what variables have the most effect on cluster values 0 and 1. Where cluster 0 is a cluster that shows which variables are more at risk of diabetes, while cluster 1 is a cluster whose value shows what variables are far from the risk of causing diabetes mellitus. Then based on the results of the cluster that has been done, random blood sugar variables become the most influential variable on the risk of developing diabetes mellitus, followed by blood sugar variables 2 hours PP, and fasting blood sugar
ANALISIS PENGARUH FAKTOR EKONOMI TERHADAP KESEJAHTERAAN MASYARAKAT: PENDEKATAN HOLISTIK PADA VARIABEL SOSIAL DAN EKONOMI Sabela, Sefilah Naurah; Valentina, Tiara; Trimono, Trimono
Kohesi: Jurnal Sains dan Teknologi Vol. 6 No. 6 (2025): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kohesi.v6i6.10025

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh faktor ekonomi terhadap kesejahteraan masyarakat menggunakan pendekatan holistik. Latar belakang penelitian ini adalah kesenjangan dalam pemahaman tentang hubungan antara variabel ekonomi, seperti pendapatan per kapita, distribusi pendapatan, dan stabilitas ekonomi makro, terhadap kesejahteraan. Metode penelitian ini melibatkan analisis data berdasarkan teori ekonomi kesejahteraan dan teori kemampuan. Hasil penelitian menunjukkan bahwa distribusi pendapatan yang lebih merata dan stabilitas ekonomi secara signifikan meningkatkan kualitas hidup masyarakat. Selain itu, integrasi variabel sosial seperti akses pendidikan dan kesehatan dengan kebijakan ekonomi terbukti efektif dalam mengoptimalkan kesejahteraan masyarakat. Temuan ini menawarkan pendekatan baru yang menekankan pentingnya sinergi antara variabel ekonomi dan sosial dalam perencanaan strategi pembangunan berkelanjutan. This study aims to analyze the influence of economic factors on societal welfare using a holistic approach. The research highlights gaps in understanding the relationship between economic variables, such as per capita income, income distribution, and macroeconomic stability, on welfare. The methodology involves data analysis using welfare economics and capability theory frameworks. Results indicate that equitable income distribution and macroeconomic stability significantly enhance the quality of life. Additionally, integrating social variables, such as access to education and healthcare, with economic policies effectively optimizes societal welfare. These findings propose a novel approach that emphasizes the synergy between economic and social variables for sustainable development planning.
Pengaruh Leverage dan Likuiditas Terhadap Kualitas Laba pada Perusahaan Sektor Makanan dan Minuman yang Terdaftar di BEI periode 2020-2023 Insania, Nichlata; Widayawati, Eny; Ikaningtyas, Maharani; Trimono, Trimono
Jurnal Bisnis Indonesia Vol 16, No 2 (2024): Jurnal Bisnis Indonesia (Edisi Spesial)
Publisher : Program Studi Ilmu Administrasi Bisnis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jbi.v16i2.4907

Abstract

Penelitian ini bertujuan untuk menguji pengaruh Leverage yang diproksikan oleh Debt to Equity (DER),dan Likuiditas yang diproksikan Current Ratio (CR) terhadap Kualitas laba pada Perusahaan Makanandan Minuman yang Terdaftar di Bursa Efek Indonesia (BEI) periode 2020-2023. Populasi yang digunakandalam penelitian ini adalah 84 perusahaan dan sampelnya adalah 16 perusahaan dengan menggunakanTeknik Purposive Sampling. Analisis yang digunakan dalam penelitian ini Analisis Regresi LinierBerganda menggunakan SPSS. Hasil penelitian ini menyatakan secara parsial Leverage yang di ukurdengan Debt to Equity (DER) dan Likuiditas yang di ukur Current Ratio (CR) berpengaruh signifikanterhadap Kualitas laba. Hasil penelitian secara simultan menunjukkan bahwa Leverage yang di ukur Debtto Equity (DER), dan Likuiditas yang di ukur Current Ratio (CR) berpengaruh terhadap Kualitas Labasedangkan kontribusi yang diberikan oleh Leverage yang di ukur Debt to Equity (DER), dan Likuiditasyang di ukur Current Ratio (CR) terhadap Kualitas Laba sebesar 97,6 %, sedangkan sisanya sebesar 2,4%di jelaskan oleh variabel lain yang tidak termasuk dalam penelitian iniKata kunci: Leverage, Likuiditas, Kualitas Laba
XportID: Website for Clustering Indonesian Export Commodities by Destination Continent using Gaussian Mixture Model Lisanthoni, Angela; Trimono, Trimono; Prasetya, Dwi Arman
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.27500

Abstract

Exports play a crucial role in driving economic growth and increasing foreign exchange reserves. However, Indonesia's export performance has not yet reached its optimal potential, as evidenced by an 11% decline in export value in 2023. The decrease is partly attributed to the limited range of export destination markets. Therefore, this study aims to analyze export trade patterns to identify the most ideal and potential market locations. The research will employ a quantitative approach, using secondary data from the Central Bureau of Statistics and the 2022 BACI dataset, focusing on the top 5 HS2 commodity types by highest export quantity. Clustering analysis is applied to group markets based on similar characteristics, identifying countries with high, medium, and low export potential for Indonesia’s export strategy. The research develops a website-based clustering system called XportID, utilizing a Gaussian Mixture Model (GMM) with the Expectation-Maximization (EM) algorithm to determine optimal cluster parameters. GMM is preferred for its flexibility and probabilistic system, providing more accurate results compared to distance-based methods. There will be 3, 4, and 5 clusters formed and then the best cluster will be selected by comparing the silhouette score obtained. Results show that the Asian continent has 5 clusters with the best value of 0.7035, the American continent has 3 clusters with the best value of 0.8165, the African continent has 3 clusters with the best value of 0.8534, the Australian continent has 3 clusters with the best value of 0.8540, and the European continent has 4 clusters with the best value of 0.8654. Overall results, the clustering system is categorized as strong structure with average value of 0.8185. Countries with high export potential include Malaysia, Philippines, South Korea, Brazil, Mexico, New Zealand, and Spain. Specifically in Africa, commodities related to HS2-15 show potential for growth.
Co-Authors Abda Abda Abdullah Abdullah Adam, Cindi Ade Irma Agustian Adiwidyatma, Afdhal Reshanda Aliya Dasa Pramesthi Amanillah, Rahmatul Amri Muhaimin Andreas Nugroho Sihananto Ardiani, Ardia Eva Arif, Farah Yusnaida Arifta, Septia Dini Aurelia, Cenditya Ayu Aviolla Terza Damaliana Aviolla Terza Damaliana Awang, Wan Suryani Wan Azni Aisyah Azzahra, Adelia Ramadhina Bainar Bainar, Bainar Bey Lirna, Cagiva Chaedar Carissa, Savvy Prissy Amellia Damaliana, Aviolla Terza Desy Miftachul Ilmi Arifin Putri Dewi, Ni Luh Ayu Nariswari Di Asih I Maruddani Di Asih I Maruddani Di Asih I Maruddani Diash, Hakam Dzakwan Dinda Putri Arnindi Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Edi Sugiyanto Fahrudin, Tresna Maulana Fairuz Luthfia Winoto Putri, Maretta Faiz, Mochammad Abudrrochman Farkhan Febri Giantara Febriyanti, Alvi Yuana Febyanti, Iin Hadi, Surjo Hadiyan Pradipta, Alvino Hasan Hendri Prabowo Herlina Herlina Hervrizal, Hervrizal I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa I Gusti Putu Asto Buditjahjanto Icha Rohmatul Jannah idhom, Mohammad Ikaningtyas, Maharani Ikaningtyas, Maharani Imelda Widya Ningrum Indira Zein Rizqin Insania, Nichlata Irawan, Tanaya Anindita Irma Amanda Putri Kartika Maulida Hindrayani Kartini Kartini Kassim, Anuar bin Mohamed Khairunisa, Adenda Khosyi, Hanun Aufa Nur Kusdani, Kusdani Kuswardana, Dendy Arizki Linggasari, Dienna Eries Lisanthoni, Angela M Zufar Irhab S Putra Maharani Ikaningtyas Maruddani, Di Asih Marwani, Arrum Mas'ad Mas'ad Maulana Pasha, Naufal Ricko Maulidiyyah, Nova Auliyatul Milla Akbarany Baktiar Putri Mohammad Idhom Mohammad Idhom Muhaimin, Amri Muhammad Muharrom Al Haromainy Muhammad Nasrudin Munoto Nabila, Nasywa Azzah Nabilah Selayanti Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nasution, Baktiar Nathania, Vannesa Ningrum, Imelda Widya Ningtiyas, Rona Wulan Novita Anggraini Nugraheni, Setiawati Oktaviani, Sheny Eka Panglima, Talitha Fujisai Prisma Hardi Aji Riyantoko Prismahardi Aji Riyantoko Putra, Andrawana Putri, Irma Amanda Putri, Nabila Rizky Amalia Putri, Nevia Desinta Rafiqah, Lailan Rafli Feandika Nugroho, Muhammad Ratna Yulistiani Renaldi, Sahat Rhomaningtias, Lina Riswanda, Mohammad Nizar Riyantoko, Prismahardi Aji Ryan Dana, Alvin Sabela, Sefilah Naurah Safira Devi, Arsita Safira, Alya Mirza Salma Namira, Alivia Saputra, Wahyu Syaifullah Jauharis Sekar Arum Melati Selly Rizkiyah Sihananto, Andreas Sonhaji, Abdulah Sugiarti, Nova Putri Dwi Suprapto, Rheinka Elyana Susrama Mas Diyasa , I Gede Syamsul Rizal Syukri Syukri Tarno Tarno Taufik, Ikbar Athallah Terza Damaliana, Aviolla Tresna Maulana Fahrudin Utami, Rianti Siswi Utriweni Mukhaiyar Valentina, Tiara Wardah Ariij Adibah Wardah, Salsabila Wibowo, Muhammad Bagas Satrio Widayawati, Eny Widayawati, Eny Widduro, Bagus Widison, Daffin Tanjiro Yuciana Wilandari yuliza, eva Zalfa Assyadida, Azizah