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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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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.
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.
MONITORING PARAMETER AIR BERBASIS IOT (INTERNET OF THINGS) Anshori, Yusuf; Parenrengi, Andi Fathur Alamsyah A.; Angreni, Dwi Shinta; Ardiansyah, Rizka; Joefrie, Yuri Yudhaswana
Foristek Vol. 13 No. 2 (2023): Foristek
Publisher : Foristek

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54757/fs.v14i2.322

Abstract

Water is a necessity for living things that have certain parameters to be consumed. This tool is made to measure the parameters of pH, temperature and turbidity of water quality and this tool is integrated with Internet of Things (IoT) technology so that sensor measurement data can be accessed anywhere and anytime. This tool implements Fuzzy Logic to generate “clean” and “unclean” values for water and uses the NodeMCU-ESP32s Module as the main controller, the PH-4502c sensor measures pH, the SKUSEN0189 sensor measures turbidity, and the DS18B20 sensor measures temperature. The results show that all sensors work well with an average error value of 2.95% for pH, 0.80% for temperature, and 21.32% for turbidity.
Implementasi Data Mining Untuk Rekomendasi Kenaikan Pangkat Pegawai Negeri Sipil Menggunakan Algoritma Naïve Bayes Pada Biro Administrasi Pimpinan Sekretariat Daerah Provinsi Sulawesi Tengah Angreni, Dwi Shinta; Susanti, Maulidia
Innovative: Journal Of Social Science Research Vol. 4 No. 1 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i1.9016

Abstract

PNS adalah kepanjangan dari Pegawai Negeri Sipil yang merupakan warga negara Indonesia yang memenuhi persyaratan tertentu untuk dipekerjakan dan diangkat sebagai Aparatur Sipil Negara secara tetap dan menduduki jabatan di pemerintahan. Penelitian ini memiliki permasalahan pada keterlambatan pengajuan berkas yang disebabkan karena banyaknya berkas yang berbeda jenis tumpang tindih, serta media penyimpanan berkas yang belum terkomputerisasi sepenuhnya memerlukan banyak waktu dan ketelitian dalam penyeleksian, sehingga menyebabkan keterlambatan dalam pengurusan kenaikan pangkat. Tujuan dari penelitian adalah untuk membuat sistem yang dapat mengimplementasikan data mining menggunakan algoritma naïve bayes dalam melakukan klasifikasi untuk rekomendasi kenaikan pangkat PNS. Berdasarkan hasil dari pengimplementasin sistem, didapatkan hasil akurasi sebesar 84.24% menggunakan pengujian algoritma confusion matrix, precision sebesar 81.34%, dan Recall sebesar 73.22%.
Analisis Penyakit Mental Menggunakan Algoritma XGBoost Landusa, Natalia Anastasya; Ardiansyah, Rizka; Nugraha, Deny Wiria; Lamasitudju, Chairunnisa; Angreni, Dwi Shinta
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 4 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i4.5135

Abstract

Kesehatan mental merupakan bagian penting dalam kesejahteraan individu, dengan gangguan mental seperti skizofrenia, bipolar, dan depresi yang dapat memengaruhi kualitas hidup. Namun, diagnosa yang akurat untuk membedakan jenis gangguan ini seringkali menjadi tantangan karena gejala yang saling tumpang tindih. Penelitian ini bertujuan untuk mengklasifikasikan tiga jenis gangguan mental menggunakan algoritma XGBoost dan mengidentifikasi fitur penting yang berpengaruh dalam proses klasifikasi. Metode yang digunakan mencakup pengumpulan data dari dataset Kaggle yang berisi 3753 data pasien dengan 53 atribut dan 3 kelas gangguan mental. Proses pre-processing dilakukan untuk menormalkan data, yang kemudian digunakan untuk melatih model XGBoost. Hasil penelitian menunjukkan akurasi model sebesar 98,67% dengan nilai precision, recall, dan F1-score yang sangat tinggi, menunjukkan bahwa XGBoost efektif dalam mengklasifikasikan gangguan mental. Fitur utama yang berpengaruh dalam klasifikasi antara lain halusinasi, pikiran atau ucapan yang tidak teratur, dan delusi. Penelitian ini menyarankan penelitian lebih lanjut untuk pengembangan fitur dan validasi klinis model ini dalam konteks dunia medis.
PERBANDINGAN AKURASI LINEAR REGRESSION DAN SUPPORT VECTOR REGRESSION DALAM PREDIKSI SUHU RATA-RATA Lesnusa, Gideon Namlea; Dwi Shinta Angreni; Ardiansyah, Rizka
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.3944

Abstract

The weather in Indonesia varies significantly and is influenced by geographical location, topography, and regional climate. Weather patterns differ between the western and eastern parts of Indonesia. This study explores time series models to predict weather data in Palu City, a region that is complex due to various weather factors. The focus is on the unique weather patterns reflected by the geography and topography of Palu City. Evaluation was conducted on time series models, including Linear Regression and Support Vector Regression (SVR), to estimate weather conditions in Palu City. The evaluation results show that the SVR model has an RMSE of 0.6302, while linear regression has an RMSE of 0.6328. This research has the potential to improve early warning and decision-making regarding extreme weather
Interaksi Augmented Reality Menggunakan Boxcollider Dalam Aplikasi Pembelajaran Bahasa Inggris Zulkifli, Zulkifli; Joefrie, Yuri Yudhaswana; Nugraha, Deny Wiria; Lapatta, Nouval Trezandy; Syahrullah, Syahrullah; Angreni, Dwi Shinta
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.v10i2.6248

Abstract

Teknologi Augmented Reality (AR) telah menjadi salah satu inovasi terdepan dalam meningkatkan pengalaman belajar interaktif. Penelitian ini mengkaji penggunaan AR dalam aplikasi pengenalan bahasa Inggris dengan memanfaatkan fitur BoxCollider untuk interaksi pengguna. Ap-likasi ini dirancang untuk membantu pengguna, terutama pelajar, dalam mengenali dan memahami kosakata bahasa Inggris melalui pengalaman visual dan interaktif. BoxCollider digunakan untuk mendeteksi interaksi antara pengguna dan objek virtual yang ditampilkan di layar, memung-kinkan respons langsung terhadap tindakan pengguna seperti menyentuh atau menggerakkan objek. Hasil penelitian menunjukkan bahwa penggunaan BoxCollider dalam AR meningkatkan keterlibatan pengguna dan memudahkan proses belajar. Pengguna dapat berinteraksi dengan berbagai objek yang mewakili kata-kata bahasa Inggris, sehingga mem-berikan konteks visual yang kuat dan mendukung pemahaman kosakata secara lebih efektif. Aplikasi ini diharapkan dapat menjadi alat bantu yang efektif dalam pengajaran bahasa Inggris, menawarkan metode bela-jar yang lebih menarik dan interaktif dibandingkan dengan metode kon-vensional
PERBANDINGAN METODE ARIMA DAN RANDOM FOREST DALAM MEMPREDIKSI HARGA EMAS BERDASARKAN PERGERAKAN MATA UANG DAN SUKU BUNGA Afifa afifa; Rizka Ardiansyah; Chairunnisa Lamasitudju; Rahma Laila; Dwi Shinta Angreni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6346

Abstract

Dalam situasi ekonomi global yang tidak stabil dan ketidakpastian politik internasional, emas tetap menjadi aset andalan bagi investor sebagai tempat berlindung yang aman. Namun, fluktuasi harga emas yang dipengaruhi oleh berbagai faktor eksternal dan internal menciptakan tantangan dalam memprediksi harga dan mengambil keputusan investasi yang tepat. Penelitian ini bertujuan untuk membandingkan akurasi prediksi harga emas dengan menggunakan dua metode, yaitu ARIMA dan Random Forest, yang mempertimbangkan data pergerakan mata uang dan suku bunga. Hasil penelitian ini menunjukkan bahwa metode ARIMA pada set data pengujian menghasilkan MAPE sebesar 4.26%, sedangkan model Random Forest menghasilkan MAPE sebesar 2.25%. Berdasarkan hasil perbandingan tersebut, dapat disimpulkan bahwa model Random Forest memiliki performa yang lebih baik dalam memprediksi harga emas dibandingkan dengan model ARIMA. PERBANDINGAN METODE ARIMA DAN RANDOM FOREST DALAM MEMPREDIKSI HARGA EMAS BERDASARKAN PERGERAKAN MATA UANG DAN SUKU BUNGA 
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