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All Journal Foristek 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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RANCANG BANGUN SISTEM INFORMASI DIREKTORAT SAMAPTA POLDA MENGGUNAKAN ALGORTIMA RC4 BERBASIS WEBSITE Harani, Makruf; Lamasitudju, Chairunnisa Ar; Angreni, Dwi Shinta; Azhar, Ryfial; Laila, Rahma; Miftah, Miftah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6312

Abstract

sistem informasi kepegawaian adalah untuk data menangani kepegawaian, hal ini sangatlah penting mengingat kebutuhan akan ada data peningkatan dan informasi. Saat ini Direktorat Samapta Polda Sulteng masih mengolah data personel terutama dengan manual, termasuk pengolahan data dan penyimpanan informasi. File sangat penting untuk menjaga data kerahasia, terutama untuk dokumen yang isinya hanya dapat diakses oleh individu yang berwenang. Tanpa tindakan pencegahan keaman, kerahasia dan intersepsi akan terancam menjadi penghambat produktivitas pekerja. Demi menjaga kerahasiaan berkas dokumen yang merupakan aset berharga Direktorat Samapta Polda Sulteng, RC4 dalam penelitian ini dimanfaatkan untuk membantu pengarsipan data dan data perlindungan. Pengujian keberhasilan sistem menggunakan Delone Dan Mclean ADALAH Model Sukses menunjukan bahwa pengguna puas dengan sistem informasi pegawai ditsamapta polda provinsi Sulawesi Tengah Menggunakan RC4 beserta kualitas informasi yang disajikan.
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.
Implementation of QR Code in A Student Attendance Information Based On WhatsApp Gateway Karnita Sumbaluwu, Harlin Feby; Angreni, Dwi Shinta; Pusadan, Mohammad Yazdi; Lamasitudju, Chairunnisa; Lapatta, Nouval Trezandy
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6308

Abstract

The attendance information system at Senior High School 7 Sigi, still uses a manual attendance system, namely writing on paper sheets. The problem that often occurs is the loss of student attendance books which causes the school to have difficulty in recapitulating attendance and also reporting attendance to parents. Another problem that occurs due to manual attendance is that parents cannot directly monitor their children's attendance at school which causes some students to skip school. The recommended solution is to use an attendance information system by utilizing QR Code technology so that student attendance is more practical and also the data storage is much safer. WhatsApp Gateway is used as a monitoring medium for parents because this system will send notifications via the WhatsApp application every time the lesson starts, effectively and in real-time. This attendance system uses the Waterfall method which starts from the planning, analysis, design and implementation stages
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.
UI/UX Design of Jepun Bali Store Product Ordering Application Using Design Thinking Method Widiani, Ni Nengah; Syahrullah, Syahrullah; Laila, Rahma; Lamasitudju, Chairunnisa Ar; Angreni, Dwi Shinta
CCIT (Creative Communication and Innovative Technology) Journal Vol 19 No 1 (2026): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v19i1.3965

Abstract

The internet as a form of technological advancement continues to develop every year and has a great influence on human activities, including in terms of sales. The Jepun Bali Store, which sells products typical of Hinduism and Balinese customs, markets its products online with Instagram, Facebook, and WhatsApp. Instagram and Facebook are used to display the catalog, while WhatsApp is used for order communication. However, this system is considered less efficient because customers have to switch applications to view products, ask questions, and order. Stock and price information is not available in real-time, and the ordering process is still done manually, making it difficult for customers. From the manager's side, manual order recording risks creating errors, while admins are often overwhelmed with handling queries across multiple platforms, which impacts customer satisfaction. This research aims to simplify the transaction process, speed up services, and increase efficiency by applying the Design Thinking method. This method helps in understanding the needs of the user, structuring problems, and producing solutions through systematic stages. The results of the design test using the System Usability Scale (SUS) method with 30 respondents obtained a score of 88.5833 out of 100, included in category A (Excellent) and considered acceptable.
Segmentasi Pelanggan Menggunakan Kerangka LRFMV dan Algoritma K-Means untuk Optimalisasi Strategi Pemasaran Wawagalang, A. Nolly Sandra; Syahrullah, Syahrullah; Ardiyansyah, Rizka; Angreni, Dwi Shinta; Pratama, Septiano Anggun; Nugraha, Deny Wiria
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.31025

Abstract

In this competitive digital era, customer behavior is key to maintaining loyalty and increasing profitability. This study aims to implement customer segmentation using the Length, Recency, Frequency, Monetary, Volume (LRFMV) approach and the K-Means algorithm to identify customer behavior characteristics and determine high-value segments. The combination of these five dimensions has rarely been used in previous studies, thus providing a new contribution to data-based customer behavior analysis. This study adopts an exploratory descriptive quantitative approach. The data used consists of 2,098 transactions from 452 customers, sourced from a public GitHub dataset. The data analysis process includes preprocessing, determining LRFMV values, and segmentation using K-Means Clustering. The Silhouette Coefficient is used to evaluate cluster quality and determine the optimal number of clusters. The results show that the best configuration is obtained at k=5 with a Silhouette value of 0.842. The findings show five customer segments with different characteristics and Customer Lifetime Value (CLV) values. Clusters 0 and 2 are categorized as Loyal Customers (L↑R↓F↑M↑V↑) with the highest CLV. Clusters 3 and 1 are Inactive New Customers (L↓R↑F↓M↓V↓) with low contribution. Cluster 4 consists of Inactive Customers (L↓R↓F↓M↓V↓), indicating overall inactivity. These segmentation results are used to develop more targeted strategies, such as loyalty programs or reactivation campaigns, to optimize marketing strategies based on customer value.
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: 10.15408/2twt5d12

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.   Abstrak Keputusan 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.
Implementation of Data Layer In Blockchain Network Using SHA256 Hashing Algorithm Sondakh, Clivent Gerhard; Ardiansyah, Rizka; Joefrie, Yuri Yudhaswana; Angreni, Dwi Shinta; Pusadan, Mohammad Yazdi
Advance Sustainable Science, Engineering and Technology Vol 6, No 2 (2024): February - April
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i2.18103

Abstract

The escalating demand for secure data management in blockchain systems has prompted the exploration of advanced cryptographic techniques. Leveraging the SHA256 hashing algorithm, this implementation aims to fortify data integrity, confidentiality, and authentication within the blockchain network. By meticulously examining the algorithm's application, the research demonstrates its efficacy in ensuring tamper-resistant data storage and retrieval, quantifying improvements in security percentages and specific metrics. The integration of SHA256 within the data layer is explored in technical detail, highlighting the concrete benefits of heightened security and immutability. The analysis discusses practical implications and delves into potential advancements in blockchain technology, offering valuable insights for researchers, developers, and practitioners seeking to bolster the robustness of data layers in blockchain networks.
Implementing Blockchain For Publishing and Verifying Digital Certificates On EduTech Maroso, Akwan; Angreni, Dwi Shinta; Ardiansyah, Rizka; Dwiwijaya, Kadek Agus
Advance Sustainable Science, Engineering and Technology Vol 6, No 2 (2024): February - April
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i2.18262

Abstract

This study investigates the application of blockchain technology in enhancing the security and authenticity of digital certificates. Addressing key challenges such as fraud and the lack of a standardized verification process, the paper proposes a comprehensive framework aimed at fortifying the integrity of digital credentials. This framework is the utilization of blockchain as a distributed ledger, serving as a tamper-proof repository for recording certification transactions. Through this decentralized ledger, each certification issuance and verification action is securely recorded, enhancing trust and transparency in the certification process. The methodology includes the integration of a decentralized ledger for immutable record-keeping  and implementation of smart contracts for automated authenticity checks, and the use of cryptographic measures to ensure data security. This approach promises significant implications for various sectors reliant on credential verification, advocating for a broader adoption of blockchain in digital certificates systems.
Co-Authors ., Rezki A Djufri, Isdar Abdul Mahatir Najar Afifa, Afifa Aldy, Moh.Fajrin Sigit Anita Ahmad Kasim Anita, Ayu Ardiyansyah, Rizka Arsita, Tiara Juli Budiman, Wahyu Darojah, Murtafiatun Deni Luvi Jayanto Deny Wiria Nugraha Dwimanhendra, Muhammad Rifaldi Dwiwijaya, Kadek Agus Hajra Rasmita Ngemba Harani, Makruf Hardianti Hardianti, Hardianti I Wayan Sudarsana Karnita Sumbaluwu, Harlin Feby Laila, Rahma Lamasitudju, Chairunnisa Landusa, Natalia Anastasya Lesnusa, Gideon Namlea Mariani Mariani Maroso, Akwan Miftah Miftah, Miftah Mohammad Yazdi Pusadan Nouval Trezandy Lapatta Parenrengi, Andi Fathur Alamsyah A. Putri, Niluh Putu Aprillia Puspitadewi Sudarsana Rahmah 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 Syaiful Hendra Ulhak, Muhamad Zia Wawagalang, A. Nolly Sandra Widiani, Ni Nengah Winarta, Ardhiansyah Wirdayanti Yunita Prastyaningsih Yuri Yudhaswana Joefrie Yusuf Anshori Zulkifli Zulkifli