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All Journal snimed Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jupiter TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Pseudocode Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Informatika dan Teknik Elektro Terapan Seminar Nasional Informatika (SEMNASIF) International Journal of Artificial Intelligence Research IKRA-ITH Informatika : Jurnal Komputer dan Informatika INTECOMS: Journal of Information Technology and Computer Science JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal Teknologi Informasi dan Pendidikan Syntax Idea JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Kajian Ilmiah Jurnal Computer Science and Information Technology (CoSciTech) Journal La Multiapp SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Mandiri IT Informatics and Computer Engineering Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science Innovative: Journal Of Social Science Research Communautaire: Journal of Community Service Jurnal Medika: Medika Jurnal Informatika Dan Tekonologi Komputer
Vitri Tundjungsari
Universitas Esa Unggul, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Analisis Penjualan Merchandise Barang Fandom KPOP Berbasis Web (E-Commerce) Dengan Metode Waterfall Studi Kasus di Toko Allaboutmerch Fauziah, Siti; Tundjungsari, Vitri
Innovative: Journal Of Social Science Research Vol. 5 No. 2 (2025): 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.v5i2.15105

Abstract

Penggunaan internet yang semakin luas dan menjurus pada dunia bisnis mulai memanfaatkan internet sebagai media penjualan dan pembelian. AllAboutMerch merupakan toko yang bergerak sebagai bidang merchandise kpop, yang mana pembelian produk dilakukan pelanggan dengan datang langsung ke toko. Untuk memberikan solusi maka dibuatlah e-commerce yang mana pelanggan dapat melakukan pembelian produk dengan mudah tanpa harus datang ke toko, serta perusahaan lebih mudah memberikan informasi kepada pelanggan yang nanti berpengaruh bagi daya jual. Sebagai dasar dan acuan untuk membuat sistem penjualan berbasis web dilakukan studi pustaka. Pembuatan web dilakukan dengan framework Laravel dan menggunakan database MySQL. Metode yang dilakukan dalam pengembangan sistem ini menggunakan metode Waterfall. Pengujian dilakukan menggunakan metode blackbox untuk mengetahui apakah terjadi error atau bug pada sistem yang dibuat. Dari pengujian blackbox mendapatkan hasil yang baik pada pengujian fungsionalitas sistem, menunjukan bahwa aplikasi dapat berfungsi dan digunakan dengan baik.
Penerapan Sistem Informasi Website Absensi dengan RFID Berbasis IoT di PT. Eksgon Brother Tundjungsari, Vitri; Prisdianto, Dendi
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 9 No. 1 (2025): IKRAITH-INFORMATIKA Vol 9 No 1 Maret 2025
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v9i1.4372

Abstract

The expansive number of laborers spread over different offices in a private retail traderequires an proficient time following framework for its workers. This causes teach andinfluences the work execution of each worker. Manual recording and participationframeworks for representatives who are still in utilize result in misplaced time and otherinfringement. Computers are information handling apparatuses that can turn this timeadministration framework into an programmed and valuable framework. In this manner,uncommon information collection is required to record participation and nonattendanceso that work exercises can be recorded precisely and in genuine time. There are numerousways to induce a great time following data framework. One way is to utilize a computerwith a web-based time following application. At PT Eksgon Brother, the framework utilizedwithin the time recording handle is continuously manual and employments a every dayparticipation book, so that it has an affect on the effectiveness and adequacy in collectingand collecting information and its calculation takes a moderately long time. With thechance of blunders and misfortune of participation information is expanding. Based on theover issues, an representative timekeeping data framework was made at PT. EksgonBrother.
Perbandingan Metode K-Means dan Hierarchical Clustering pada Rekomendasi Musik Berbasis Audio Spotify Features Sistem Sinulingga, Samuel Mahesa; Farrel Reyhan Putra; Andhika Dwi Rachmawanto; Michael Jeconiah Yonathan; Valentino Wijaya; Vitri Tundjungsari
Informatics and Computer Engineering Journal Vol 6 No 1 (2026): Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/icej.v6i1.11895

Abstract

Penelitian ini membandingkan metode K-Means dan Hierarchical Clustering dalam sistem rekomendasi musik berbasis audio features Spotify. Dataset yang digunakan berasal dari Spotify Tracks Dataset yang terdiri dari sekitar 114.000 lagu, kemudian melalui tahap pra-pemrosesan diperoleh sekitar 81.000 lagu valid. Untuk efisiensi komputasi, digunakan 5.000 lagu sebagai data eksperimen. Clustering dilakukan menggunakan 6 cluster dengan sembilan atribut audio. Evaluasi menggunakan Silhouette Score dan Davies–Bouldin Index menunjukkan bahwa K-Means memperoleh nilai Silhouette Score 0,1900 dan Davies–Bouldin Index 1,4445, sedangkan Hierarchical Clustering memperoleh nilai Silhouette Score 0,1782 dan Davies–Bouldin Index 1,4522. Hasil ini menunjukkan bahwa K-Means menghasilkan cluster yang lebih kompak. Sistem rekomendasi yang dibangun mampu memberikan rekomendasi lagu yang relevan berdasarkan kemiripan karakteristik audio.
Rancang Bangun Sistem Inventori Berbasis Web Dengan Prediksi Penjualan Menggunakan Time Series Forecasting Rahmat, Ridwan; Tundjungsari, Vitri
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10575

Abstract

Effective inventory management is a crucial aspect of company operations to predict future stock requirements and product demand. This research aims to design and develop a web-based inventory system with sales prediction using Time Series Forecasting algorithms at CV Adio Loop Engineering. The development method used is waterfall with Long Short-Term Memory (LSTM) approach as a prediction model based on historical inventory transaction data. The system has comprehensive features including dashboard with information on total products, purchases, sales, categories, and suppliers; prediction module for selecting products and prediction types (demand/stock) with time estimation; master data for managing categories, products, and suppliers; transaction modules for purchasing, sales, and inventory; stock movement; low stock alerts; inventory reports; and human resource management with login/logout security system. All modules are equipped with complete CRUD functions. Test results show that the system is capable of providing accurate predictions and improving operational efficiency in inventory management and future stock requirement planning.
PENGEMBANGAN SISTEM SPEECH EMOTION RECOGNITION BERBASIS DEEP LEARNING WAV2VEC2.0 UNTUK RESPONS EMOSIONAL KARAKTER KUCING INTERAKTIF DI GAME UNITY Farrel Reyhan Putra; Dzaky, Hafidz Muhammad; Putratama, Maheswara; Fikri, Mochammad Rabee Fathi Al; Tundjungsari, Vitri
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.9064

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem Speech Emotion Recognition (SER) berbasis deep learning yang mampu mengenali emosi suara dan mengimplementasikannya pada sebuah game interaktif menggunakan Unity. Model SER dibangun dengan memanfaatkan arsitektur Wav2Vec 2.0 yang telah dipra-latih (pretrained) dan dilakukan fine-tuning menggunakan dataset CREMA-D dengan empat kelas emosi, yaitu angry, happy, neutral, dan sad. Data dibagi dengan rasio 80% untuk pelatihan dan 20% untuk validasi. Hasil pengujian menunjukkan bahwa model mampu mencapai nilai akurasi validasi maksimum sebesar 78–79% dengan weighted F1-score sebesar 0,79. Analisis confusion matrix memperlihatkan bahwa kelas angry memiliki tingkat pengenalan tertinggi, sementara kesalahan klasifikasi paling sering terjadi antara kelas neutral dan sad akibat kemiripan karakteristik prosodi. Model yang telah dilatih kemudian diekspor ke format ONNX dan berhasil diintegrasikan ke dalam game Unity untuk mendeteksi emosi suara pemain secara real-time. Hasil implementasi menunjukkan bahwa sistem mampu memberikan respons karakter yang adaptif berdasarkan emosi suara pengguna, sehingga meningkatkan interaksi dalam permainan.
No-code workflow automation for meeting minutes generation using scrum Syafika Zalfanissa Dila; Tundjungsari, Vitri
Jurnal Mandiri IT Vol. 14 No. 3 (2026): Jan: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i3.500

Abstract

The increasing frequency of online meetings has intensified the need for efficient and consistent meeting documentation. However, the preparation of meeting minutes is still commonly performed manually, resulting in delayed documentation, inconsistent formats, and increased workload. This study aimed to develop and evaluate a no-code workflow automation system for meeting minutes generation using the Scrum framework. The proposed system integrated a no-code automation platform with an AI-based text summarization service to automatically process meeting transcripts and generate structured meeting minutes consisting of key discussion points, decisions made, and action items. The system was developed using an iterative Scrum-based approach, allowing continuous feedback and refinement throughout the development process. System evaluation focused on efficiency and usability by comparing the automated process with manual documentation. The results showed that the proposed system reduced the meeting minutes preparation time from approximately 30–45 minutes per meeting (manual process) to 2–5 minutes per meeting (automated workflow), while improving consistency and clarity of the output. In addition, the no-code approach enabled non-technical users to operate and maintain the system without requiring programming skills This study demonstrated that combining no-code workflow automation, AI-based text summarization, and agile development practices can provide an effective and practical solution for automating meeting documentation in organizational environments.
SISTEM PENDUKUNG KEPUTUSAN PENGIRIMAN BUS BERBASIS AIOT MENGGUNAKAN INTEGRASI YOLOV8 DAN GOOGLE DISTANCE MATRIX API Amir, Arinal Dzikrul Haqqy; Evanza, Raihan; Tundjungsari, Vitri; Julianto, Eric
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 1 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/ywcqtg69

Abstract

Kalkulasi penumpang dan estimasi kepadatan kerumunan yang akurat memainkan peran penting dalam sistem transportasi publik cerdas untuk meningkatkan keselamatan, kualitas layanan, dan efisiensi operasional. Pendekatan berbasis visi yang memanfaatkan model deep learning, khususnya You Only Look Once (YOLO), telah diadopsi secara luas untuk deteksi dan pelacakan penumpang secara real-time karena kecepatan deteksi dan akurasinya yang tinggi. Namun, tantangan seperti oklusi, variasi skala, dan sudut pandang kamera yang terbatas tetap menjadi kendala signifikan, terutama di halte bus yang padat dan lingkungan transportasi umum. Untuk mengatasi keterbatasan ini, studi terbaru telah mengintegrasikan arsitektur Internet of Things (IoT) dengan analitik video untuk memungkinkan pemantauan arus penumpang secara berkelanjutan. Dalam penelitian ini, deteksi kerumunan berbasis YOLO dikombinasikan dengan Google Distance Matrix API untuk mengestimasi waktu tempuh dan jarak antar halte bus, sehingga memungkinkan rekomendasi pengiriman armada otomatis berdasarkan kondisi kepadatan secara real-time. Kerangka kerja berbasis AIoT yang diusulkan mendukung pengambilan keputusan berbasis data untuk operasional bus cerdas, meningkatkan responsivitas, mengurangi penumpukan penumpang, serta mengoptimalkan penjadwalan transportasi publik. Kata Kunci: AIoT-Enabled Public Transport Surveillance, YOLOv8, Sistem Transportasi Cerdas, Estimasi Kepadatan Kerumunan Real-Time, Google Distance Matrix API.
Analisis Komparatif K-Means dan Hierarchical Clustering Menggunakan Validasi Internal Clustering Awaludin, Shafwan; Agustian, Andri; Capricorna, Epsilona Katiga; Ilham, Muhammad Raihan; Rumi, Nur Ananda; Tundjungsari, Vitri
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 18 No 1 (2026): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18384228

Abstract

Clustering ialah salah satu pendekatan unsupervised learning yang bertujuan mengelompokkan informasi bersumber pada tingkatan kemiripan karakteristiknya tanpa memakai label kelas. Tata cara K- Means serta Hierarchical Clustering jadi algoritma yang kerap digunakan sebab konsepnya simpel dan gampang diimplementasikan. Riset ini bertujuan menyamakan kinerja tata cara K- Means serta Hierarchical Clustering memakai validasi internal cluster buat memperhitungkan mutu hasil pengelompokan. Dataset yang digunakan diperoleh dari salah satu web penyedia informasi terbuka berbasis website yang sediakan informasi numerik serta sudah banyak dimanfaatkan pada riset informasi mining. Proses riset dimulai dengan akuisisi informasi, preprocessing, pelaksanaan algoritma K- Means serta Hierarchical Clustering, dan penilaian hasil clustering memakai validasi internal berbentuk Silhouette Coefficient, Davies- Bouldin Index, serta Calinski- Harabasz Index. Hasil pengujian menampilkan kalau tiap- tiap tata cara mempunyai ciri yang berbeda pada pembuatan cluster. K- Means cenderung menciptakan cluster yang lebih kompak, sebaliknya Hierarchical Clustering membagikan visualisasi ikatan antar informasi yang lebih jelas lewat dendrogram. Nilai validasi internal menampilkan tata cara dengan mutu clustering terbaik tergantung pada struktur informasi yang digunakan. Riset ini diharapkan bisa jadi rujukan dalam pemilihan tata cara clustering yang cocok bersumber pada ciri informasi serta kebutuhan analisis.
Systematic Literature Review: Teknik Visualisasi Data Untuk Komunikasi Informasi Yang Efektif Di Berbagai Bidang Erlangga, Satrio Adhiyatama; Assifaa, Rr Nur; Tundjungsari, Vitri
Jurnal Kajian Ilmiah Vol. 26 No. 1 (2026): Januari 2026
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/z0az9n16

Abstract

Data visualization (DV) has emerged as a vital mechanism for transforming complex data into comprehensible visual narratives. It has become an indispensable tool for effective information communication across various disciplines, including science, education, journalism, business, `healthcare, and public policy. This paper presents a Systematic Literature Review (SLR) analyzing 24 peer-reviewed publications sourced from Google Scholar (2015–2025). The synthesis indicates that well-designed visualizations, ranging from traditional charts to sophisticated interactive dashboards and narrative displays, significantly enhance user understanding, information retention, and interpretive accuracy. Nonetheless, challenges remain concerning cognitive overload, accessibility issues, and the lack of standardized measures for visualization literacy. The review introduces an integrative framework that categorizes visualization methods, tools, and contextual applications, providing insights and recommendations for future research aimed at advancing inclusivity, interpretability, and communicative efficacy across a variety of disciplines.
Dampak Digitalisasi Sistem Manajemen Pabrik Garmen Skala Menengah Terhadap Kinerja Keuangan: Evaluasi Akurasi Pembukuan Dan Ketepatan RAB (Rencana Anggaran Belanja) Muflih, Rizky; Julio Arky Aquino Reyaan; Vitri Tundjungsari; Mulyanto, Erwan
Jurnal Kajian Ilmiah Vol. 26 No. 1 (2026): Januari 2026
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/ckyfte69

Abstract

Medium-scale garment factories often rely on paper records that are prone to errors, slow processes, and hinder accurate project cost tracking. This affects the accuracy of financial record-keeping and the precision of the Bill of Quantities (BOQ). This study assesses the impact of digitalisation through an integrated application in the garment industry on these key metrics. Using a pre-and-post approach on a single case (action research), the study examines a factory transitioning from a manual to a digital system. The intervention includes: linking attendance and payroll with projects; tracking monthly and project-specific expenses; managing stock (in/out) with automatic cost recording; calculating BOQ per project; comparing BOQ with actuals based on proportional overhead allocation; and calculating profit per project. The main indicators include: (i) financial record accuracy—ensuring consistency across modules (Attendance–Payroll–Expenses–Stock), accurate material cost recording, and easy export for summaries; (ii) BOQ accuracy—the difference between planned and actual costs in categories such as Materials, Labour, Overheads, and total project costs. Data from application logs and reports show that after implementation, errors and duplicate payments decreased; labour cost transparency increased; stock discrepancies and material cost variances reduced due to linking outgoing transactions with project costs; and the gap between BOQ and actuals narrowed. The digital system integrating BOQ, Production, Stock, Expenses, Attendance/Payroll, and Profit improves data quality and cost management, thereby enhancing the accuracy of financial records and BOQ in medium-scale garment factories. These findings support the development of standardised processes and data-driven decision-making for cost control.
Co-Authors A.A. Ketut Agung Cahyawan W Abdillah, Alfina Poetri Agung Ginanjar Agustian, Andri Ahmad Zidane Arrasyid Alfi Syahrian Amir, Arinal Dzikrul Haqqy Andhika Dwi Rachmawanto Andita, Ai Selpia Andreas Martinino Watratan Assifaa, Rr Nur Awaludin, Shafwan Bawiling, Hendry Capricorna, Epsilona Katiga Christopher Mathew Putra Daniel Prasetiyo Dodi Darmawan Darren Jusman Dzaky, Hafidz Muhammad Erlangga, Satrio Adhiyatama Evanza, Raihan Farrel Reyhan Putra Fikri, Mochammad Rabee Fathi Al Hafidz Muhammad Dzaky Ichwani, Arief Ilham, Muhammad Raihan Indra Saputra Irsyal, M.Dzikri Julianto, Eric Julio Arky Aquino Reyaan Kus Hendrawan Muiz M Naufal Arriz Martinino Watratan, Andreas Michael Jeconiah Yonathan Muflih, Rizky Muhamad Bahrul Ulum Mulyanto, Erwan Nasir, Alfian Natagama, Muhammad Arif Billah Nicholas Leonardo Ningrat, Sultan Wahyu Nizirwan Anwar Pamuji, Sonny Permatasari, Widya Indah Pratama, Ivan Febrian Prisdianto, Dendi Putratama, Maheswara Qomariyah Qomariyah Rafael Austin Rahmat, Ridwan Rino Sumiarno Rumi, Nur Ananda Rusman, Fericho Ryanto, Kamil Samuel Yahya Sinulingga, Samuel Mahesa Siti Fauziah Suhaeri Suhaeri Syafika Zalfanissa Dila Tambunan, Swandi Valentino Wijaya Widodo, Agung Mulyo Yahya, Ariel Rafael Yulhendri Yulhendri