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All Journal Foristek RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal EDUMATIC: Jurnal Pendidikan Informatika ScientiCO : Computer Science and Informatics Journal InPrime: Indonesian Journal Of Pure And Applied Mathematics IAIC Transactions on Sustainable Digital Innovation (ITSDI) Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Locus Penelitian dan Pengabdian Innovative: Journal Of Social Science Research The Indonesian Journal of Computer Science
Dwi Shinta Angreni
Universitas Tadulako
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Other

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ANALISIS EVOLUSI EKOSISTEM PERANGKAT LUNAK OPEN SOURCE : TINJAUAN PUSTAKA SISTEMATIS Angreni, Dwi Shinta; Prastyaningsih, Yunita
ScientiCO : Computer Science and Informatics Journal Vol 2, No 1 (2019): Scientico : April
Publisher : Fakultas Teknik, Universitas Tadulako

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Abstract

The development of an Open Source Software (OSS) can influence the development of other Open Source Systems. The relationship between OSS is often called an ecosystem, there are several aspects to the OSS ecosystem that can affect ecosystem evolution in the software. This study reports a systematic literature review on the influence of several aspects of the OSS ecosystem on the evolution of OSS. The Sistematic Literature Review method based on Kitchenham was used to analyze 1099 articles published in leading journals and conferences. The Results showed that Social aspects have a significant impact on ecosystem evolution, where communication between communities in an OSS ecosystem influences aspects of contributions and dependencies that encourage an ecosystem to develop and evolve.
IMPLEMENTASI METODE TOPSIS DALAM PENENTUAN CALON TRANSMIGRAN DI INDONESIA Ngemba, Hajra Rasmita; Angreni, Dwi Shinta; Winarta, Ardhiansyah; Hendra, Syaiful; A Djufri, Isdar
ScientiCO : Computer Science and Informatics Journal Vol 3, No 2 (2020): Scientico : November
Publisher : Fakultas Teknik, Universitas Tadulako

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Abstract

The population in Indonesia is growing, causing population density in an area. One of the impacts of population density in an area is that it causes job opportunities in that area to become smaller, so that the unemployment rate increases. To overcome this problem, the government moved or spread the population from a densely populated area to another area with a small population with the aim of improving the economy and opening up agricultural land in the region. However, problems in the field include the limited quota of transmigrant participants from the central government. So it makes it difficult to choose or make decisions to determine potential transmigration participants. This study aims to assist the government in making decisions to determine suitable transmigrant candidates using the TOPSIS(Technique for Order Preference By Similiarity To Ideal Solution) method. Application development using the prototype method with blackbox testing. Decision Support System Applications Determining prospective transmigrants can assist users (government) in determining eligible transmigrant candidates with the criteria desired by the government.
Aplikasi Antrian Pasien Pada Dokter Praktek Umum Menggunakan Metode FIFO (First In First Out) Berbasis Android Hardianti, Hardianti; Hendra, Syaiful; Kasim, Anita Ahmad; Azhar, Ryfial; Angreni, Dwi Shinta; Ngemba, Hajra Rasmita
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 1 (2023): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i1.1478

Abstract

Currently, there are so many services in Indonesia. One of the services in the health sector is the practice of general practitioners. Services that occur at the practice of general practitioners, namely dr. Zaki Mubarak and dr. Subhan Habibi, located in Palu, often has complaints because it is still ineffective where getting these services is still done manually by means of patients coming in person and taking a queue based on the order of seats then one by one they will be served. This causes patient discomfort in waiting. To make it easier for patients who want to seek treatment, a system is needed, with this; an Android-based patient queuing application for general practice doctors was made. The application of the method used in building the system is the FIFO queuing method where patients who register earlier get medical services first. Then the average waiting time is calculated where the results obtained will be used as an estimate of the waiting time for the next patient. The application development method in this research used the prototype method and application testing uses the black box testing method. The results of this research are the application of patient queues for general practice doctors based on Android which is built to be able to take queues anywhere and anytime and obtain some information including doctor’s practice schedules, queue numbers, running queues, and estimated waiting times so that patients can estimate arrival time without having to wait long. Based on system testing with black box, the results show that the functional system is running well. Based on the average waiting time calculation, from the 60 queue data tested, the result is that the distance between queue 1 and the order is around 5 minutes.
Geographic Information System Using Node Combination Based on Dijkstra's Algorithm for Determining the Shortest Tsunami Evacuation Route in Palu Bay: Rancang Bangun Sistem Informasi Geografis Menggunakan Kombinasi Node Berbasis Algoritma Dijkstra Pada Penentuan Jalur Terpendek Evakuasi Tsunami Di Teluk Palu Angreni, Dwi Shinta; Budiman, Wahyu; Anshori, Yusuf; Wirdayanti, Wirdayanti; Bintana, Rizqa Raaiqa
Foristek Vol. 14 No. 2 (2024): Foristek
Publisher : Foristek

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54757/fs.v14i2.455

Abstract

Indonesia, an archipelagic country situated on the Equator and surrounded by the Ring of Fire, is highly susceptible to natural disasters such as tectonic earthquakes and volcanic eruptions. Palu, one of Indonesia's seismically active regions, is traversed by the Palu-Koro Fault, which has the potential to trigger strong earthquakes and tsunamis, as evidenced by the 7.4-magnitude earthquake in September 2018. To mitigate disaster risks, it is crucial to understand community vulnerability and utilize Geographic Information System (GIS) technology. This study designs a GIS-based system using the modified Dijkstra's Algorithm to determine the shortest tsunami evacuation routes in Palu. Testing results indicate that the system is effective, with a user satisfaction rate of 89.33% and satisfactory route prediction accuracy compared to Google Maps. The system can be relied upon to help communities find the nearest evacuation routes, thereby enhancing safety and preparedness in the face of potential disasters.
Optimizing User Interface of MBKM Information System & Academic Services using Design Thinking Method (Case Study: Tadulako University) Reinaldy Mansa, Jeremy; Anggun Pratama, Septiano; Wirdayanti, Wirdayanti; Angreni, Dwi Shinta
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 6 No 1 (2024): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v6i1.676

Abstract

This study addresses the usability challenges faced by Tadulako University's MBKM & Academic Services Information System (SITAMPAN), developed in response to the Ministry of Education’s Merdeka Belajar - Kampus Merdeka (MBKM) initiative. By applying a structured Design Thinking approach, this research seeks to present a novel solution for enhancing user experience and system usability in educational information systems. Through the System Usability Scale (SUS) and User Experience Questionnaire (UEQ) evaluations, initial findings indicated a low usability score (SUS: 41.75, grade "F"), categorizing the system as a "Detractor" in the Net Promoter Score (NPS) framework. Following the implementation of user centered design improvements, the SUS score increased substantially to 86.25 ("A" grade), with NPS shifting to a "Promoter" classification, while UEQ scores showed marked improvement across all metrics. This study demonstrates the effectiveness of Design Thinking in systematically addressing and optimizing the user experience, providing valuable insights for future information system developments in educational contexts.
Donor Segmentation Analysis Using the RFM Model and K-Means Clustering to Optimize Fundraising Strategies ., Rezki; Lapatta, Nouval Trezandy; Ardiansyah, Rizka; ., Wirdayanti; Angreni, Dwi Shinta
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8464

Abstract

This study aims to segment donors using the Recency, Frequency, Monetary (RFM) model and the K-Means algorithm to optimize fundraising strategies. The RFM model is used to measure donor engagement through three dimensions: Recency (the last time a donation was made), Frequency (the frequency of donations), and Monetary (the amount of donations). By utilizing RFM scores, donors are then grouped using the K-means algorithm to generate more specific donor segments. This study was conducted using donation data from a non-profit organization, focusing on strategies to improve donor loyalty and donation frequency. The segmentation results identified several key segments, including Loyal Donors, New Donors, Potential Donors, and Low-Priority Donors. Each segment exhibits different donation behavior characteristics and requires a different strategic approach. The implementation of these segmentation results is expected to help the organization design more effective communication strategies and donation programs, as well as improve donor retention and lifetime value. Additionally, this study identifies the potential for enhancing the analytical model for broader applications in the future. This research contributes to non-profit organizations by offering a more efficient approach to managing donor relationships.
Optimization of Urban Waste Collection Routes Using the Held-Karp Algorithm in a Web and Mobile-Based System Arsita, Tiara Juli; Lapatta, Nouval Trezandy; Joefri, Yuri Yudhaswana; Angreni, Dwi Shinta; Pratama, Septiano Anggun
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8832

Abstract

In 2023, the Environmental Agency of Palu City recorded a total waste production of 97,492 tons, of which 10.4% was plastic waste. The Palu City Government operates a fleet of garbage trucks on a predetermined collection schedule. However, garbage bins frequently overflow before their scheduled pickup, resulting in extended waste accumulation and inefficiency. This study proposes a web and mobile-based system to enhance waste management by integrating bin condition reporting and shortest route calculation for collecting full bins. The Held-Karp algorithm is utilized to address the Travelling Salesman Problem (TSP) for determining optimal collection routes. The system was developed using Golang, Flutter, ReactJS, and a MySQL database. API functionality was validated using Postman, and overall system functionality was tested using the black-box method. A case study involving 8 test points (1 starting point, 10 waste collection points, and 1 endpoint) demonstrated that the proposed system reduces travel time by up to 21.74%, costs by 22.29%, fuel consumption by 21.16%, and distance traveled by 21.16% compared to conventional methods. These results highlight the potential of the system to significantly optimize waste collection operations and support sustainable urban waste management practices.
Pemanfaatan TOPSIS (Technique For Order Preference By Similarity To Ideal Solutions) untuk Rekomendasi Objek Wisata di Provinsi Sulawesi Tengah Ulhak, Muhamad Zia; Pratama, Septiano Anggun; Ardiansyah, Rizka; Angreni, Dwi Shinta
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4404

Abstract

Tourism is one of the key sectors in driving economic growth in Central Sulawesi. To support the enhancement of tourism, this research developed a web-based decision support system using the TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) to provide tourism destination recommendations. The system assists users in selecting tourist destinations based on several relevant criteria, such as facilities, accessibility, cost, cleanliness, and safety. By applying the TOPSIS method, the system can rank tourism destinations by comparing the distances between positive and negative ideal solutions. This implementation is expected to help tourists make more informed and accurate decisions regarding the destinations they wish to visit and contribute positively to the development of tourism in Central Sulawesi.
Sentiment Analysis for the 2024 DKI Jakarta Gubernatorial Election Using a Support Vector Machine Approach Mariani, Mariani; Angreni, Dwi Shinta; Nur, Sri Khaerawati; Rinianty, Rinianty; Jayanto, Deni Luvi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9260

Abstract

This study analyzes public sentiment regarding candidates in the 2024 DKI Jakarta Gubernatorial Election utilizing a Support Vector Machine (SVM) approach. Recognizing the pivotal role of social media, particularly Twitter, in shaping public opinion, the research addresses the challenges of processing large volumes of unstructured data. Through systematic data preprocessing and feature extraction, the SVM model was applied, achieving a sentiment classification accuracy of 70%. The analysis revealed a distribution of sentiments where 36.1% of comments were positive, 33.4% negative, and 30.5% neutral. These findings illustrate the complexities of public discourse surrounding key political events, highlighting the model's efficacy and the nuances of sentiment detection. Moreover, discussions on model limitations elucidate areas for enhancement, suggesting future avenues including the adoption of more sophisticated algorithms and improved data processing techniques. This research contributes to the understanding of voter sentiment dynamics in a significant electoral context, providing insights that may assist campaign strategies and political analyses in Indonesia.
A Study on Sentiment Analysis of Public Response to The New Fuel Price Policy In 2022: A Support Vector Machine Approach Putri, Niluh Putu Aprillia Puspitadewi Sudarsana; Angreni, Dwi Shinta; Sudarsana, I Wayan
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 7, No 1 (2025)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v7i1.42717

Abstract

The Indonesian government's decision to raise fuel prices in 2022, following a global surge in crude oil prices, triggered widespread public debate. Understanding public sentiment toward such policy decisions is essential for determining the appropriate timing of implementation while minimizing negative reactions. This study aims to classify public sentiment regarding the fuel price hike using the Support Vector Machine (SVM) algorithm. Data were collected from Twitter through web scraping using the SNScrape library in Python. A total of 3,000 tweets were gathered and underwent preprocessing steps such as case folding, tokenization, stopword removal, and stemming. The classification model was built in Google Colab using the SVM algorithm to categorize tweets as positive (+) or negative (–). Model performance was evaluated using a confusion matrix, achieving an accuracy of 81.0%. The results showed that 63.6% of public responses were negative, while 36.4% were positive. Additionally, it was observed that the accuracy converged to 81.1% as the number of training iterations increased. The findings were presented through word clouds and pie charts to enhance interpretability, and a simple graphical user interface (GUI) was developed for user interaction. The study indicates that the government’s repeated delays in implementing the price adjustment may have reflected sensitivity to public sentiment. This research demonstrates the potential of sentiment classification as a tool for evidence-based policymaking, offering insights into the social dynamics surrounding policy changes. Future research could expand by incorporating multi-class sentiment categories or real-time data for dynamic policy evaluation.Keywords: Fuel price; Public opinion; Sentiment analysis; Social media; SVM. AbstrakKeputusan pemerintah Indonesia untuk menaikkan harga bahan bakar minyak pada tahun 2022 dan disusul oleh lonjakan harga minyak mentah global, memicu perdebatan publik yang meluas. Memahami sentimen publik terhadap keputusan kebijakan tersebut sangat penting untuk menentukan waktu implementasi yang tepat untuk meminimalkan reaksi negatif. Penelitian ini bertujuan untuk mengklasifikasikan sentimen publik terhadap kenaikan harga bahan bakar minyak menggunakan algoritma Support Vector Machine (SVM). Data dikumpulkan dari Twitter melalui web scraping menggunakan pustaka SNScrape dalam bahasa Python. Sebanyak 3.000 tweet dikumpulkan dan dilakukan tahap praproses seperti case folding, tokenization, stopword removal, dan stemming. Model klasifikasi dibangun di Google Colab menggunakan algoritma SVM untuk mengkategorikan tweet sebagai positif (+) atau negatif (–). Kinerja model dievaluasi menggunakan matriks confusion dan mencapai akurasi 81,0%. Hasil penelitian menunjukkan bahwa 63,6% tanggapan publik bersifat negatif, sedangkan 36,4% bersifat positif. Selain itu, akurasi konvergen menjadi 81,1% seiring dengan peningkatan jumlah iterasi pelatihan. Temuan tersebut disajikan melalui word cloud dan diagram pai untuk meningkatkan interpretabilitas, dan graphical user interface (GUI) sederhana dikembangkan untuk interaksi pengguna. Studi ini menunjukkan bahwa penundaan berulang pemerintah dalam menerapkan penyesuaian harga mungkin mencerminkan kepekaan terhadap sentimen publik. Penelitian ini menunjukkan potensi klasifikasi sentimen sebagai alat untuk pembuatan kebijakan berbasis bukti, yang menawarkan wawasan tentang dinamika sosial seputar perubahan kebijakan. Penelitian di masa mendatang dapat diperluas dengan menggabungkan kategori sentimen multikelas atau data waktu nyata untuk evaluasi kebijakan yang dinamis.Kata Kunci: Bahan bakar; Opini public; Analisis sentiment; Mesia social; SVM. 2020MSC: 62H30, 91D30.
Co-Authors ., Rezki A Djufri, Isdar Abdul Mahatir Najar Afifa Afifa Anita Ahmad Kasim Ardiyansyah, Rizka Arsita, Tiara Juli Budiman, Wahyu Deni Luvi Jayanto Deny Wiria Nugraha Dwiwijaya, Kadek Agus Hajra Rasmita Ngemba Hardianti Hardianti, Hardianti I Wayan Sudarsana Laila, Rahma Lamasitudju, Chairunnisa Landusa, Natalia Anastasya Lesnusa, Gideon Namlea Makruf Harani Mariani Mariani Maroso, Akwan Miftah Miftah Moh.Fajrin Sigit Aldy Mohammad Wandy Mohammad Yazdi Pusadan Muhammad Rifaldi Dwimanhendra Nouval Trezandy Lapatta Parenrengi, Andi Fathur Alamsyah A. Putri, Niluh Putu Aprillia Puspitadewi Sudarsana Rahma Laila Reinaldy Mansa, Jeremy Rinianty, Rinianty Rizka Ardiansyah Rizqa Raaiqa Bintana Ryfial Azhar Sahril Sahril Septiano Anggun Pratama Sondakh, Clivent Gerhard Sri Khaerawati Nur Susanti, Maulidia Syahrullah Syahrullah Syahrullah Syaiful Hendra Ulhak, Muhamad Zia Wawagalang, A. Nolly Sandra Widiani, Ni Nengah Winarta, Ardhiansyah Wirdayanti Wirdayanti Wirdayanti Yunita Prastyaningsih Yuri Yudhaswana Joefrie Yusuf Anshori Zulkifli Zulkifli