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All Journal Jurnal Teknologi Terpadu Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Teknologi Informasi dan Multimedia Yayasan Cita Cendikiawan Al Khwarizmi Jurnal Informatika Terpadu Indonesian Journal Computer Science (ijcs) Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science DBESTI: Journal of Digital Business and Technology Innovation Jurnal Teknoinfo
Nabarian, Tifanny
Sekolah Tinggi Teknologi Terpadu Nurul Fikri
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Perancangan Prototype Aplikasi Mobile Ridesolve untuk Memperbaiki Akses Transportasi Mahasiswa Menggunakan Metode Design Sprint Afifah, Raihana Cindy; Nabarian, Tifanny; Munir, Sirojul
DBESTI: Journal of Digital Business and Technology Innovation Vol 1 No 2 (2024): November, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v1i2.1370

Abstract

Students at Sekolah Tinggi Teknologi Terpadu Nurul Fikri (STT-NF) encounter challenges in accessing affordable and reliable transportation to and from campus, which complicates their search for convenient and cost-effective transport options. This study aims to develop a mobile application with an intuitive and user-friendly interface to enhance the overall transportation experience for students. Employing the Design Sprint methodology, the research follows a structured process involving understanding, diverging, deciding, prototyping, and validating phases. User requirements were gathered through surveys and interviews, and the prototype was created and tested using Figma. Usability testing utilized the System Usability Scale (SUS). The outcome of this study is RideSolve, a mobile app prototype featuring functionalities such as ride booking, driver selection, and ride history viewing. Usability testing revealed a high SUS score of 82, indicating positive user reception and effective fulfillment of their needs. Ultimately, the RideSolve prototype is anticipated to notably enhance transportation convenience for STT-NF students by offering a dependable and user-friendly solution.
Pengembangan Sistem Backend Rekomendasi Produk dengan Collaborative Filtering Berbasis Singular Value Decomposition Waluyo, Ahmad; Nabarian, Tifanny; Rosyidi, Lukman
DBESTI: Journal of Digital Business and Technology Innovation Vol 2 No 2 (2025): November, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v2i2.2003

Abstract

Recommendation systems have become a crucial component in enhancing user experience on e-commerce platforms, including PT Renos Marketplace Indonesia. However, the existing recommendation system was still random and unable to adapt to user preferences. This study aims to develop a product recommendation backend system based on Collaborative Filtering using the Singular Value Decomposition (SVD) algorithm. The methodology employed is Research and Development (R&D) with an Agile Scrum approach, along with performance evaluation using RMSE, MSE, and MAE metrics. The system was developed using FastAPI, PostgreSQL, and Python, and its functionality was tested using black-box testing methods. The implementation results show an RMSE of 0.1886, MSE of 0.0366, and MAE of 0.1242, indicating excellent prediction accuracy. Additionally, a user perception survey involving 11 internal respondents showed an average satisfaction score above 80%, with the highest score of 90.91% for the statement indicating that the system increases purchase likelihood. These findings demonstrate that the SVD algorithm is effective in generating relevant and personalized recommendations. The study concludes that the developed backend recommendation system successfully improves the efficiency and relevance of product recommendations and opens opportunities for further development through the integration of more complex user behavior data.
Development of A Telegram Chatbot for Final Project Services Using the Design and Development Research (DDR) Approach Laisa Nurin Mentari; Tifanny Nabarian; Slamet Santoso; Dymas Sutrysno
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i1.1211

Abstract

Final project administration in higher education involves multiple stakeholders and frequently encounters issues such as delayed information delivery, document inconsistency, and repetitive student inquiries. These problems may increase administrative workload and hinder the final project process. This study aims to analyze user needs and develop a chatbot as an academic information service for final project administration at STT Terpadu Nurul Fikri. The research adopts the Design and Development Research (DDR) approach, focusing on two main stages: Need Analysis and Design and Development. User needs were identified through interviews, observations, and document analysis. Based on the findings, a Telegram-based chatbot was designed and developed using Python and Telegram Bot API integration. The chatbot provides academic etiquette guidelines, general final project information, implementation procedures, schedules, references, and frequently asked questions. The novelty of this study lies in its systematic emphasis on the needs analysis stage as a foundational element for academic chatbot development, which has received limited attention in prior studies. The results indicate that the chatbot has been successfully implemented and deployed as a prototype, serving as a basis for future evaluation and further system enhancement.
Efektivitas IndoBERT pada Klasifikasi Sentimen Evaluasi Dosen: Studi Komparatif Support Vector Machine dan Naive Bayes Tifanny Nabarian; Maryam Hasnaa' Syamila; Salman El Farisi; Ananto Dwi Saputro
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 2 (2026): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i2.984

Abstract

Sentiment analysis of student feedback plays an important role in evaluating the quality of teach-ing and learning processes in higher education. Qualitative comments in Student Evaluation of Teaching (SET) provide deeper insights than numerical ratings. However, they are expressed in unstructured textual form, making large-scale analysis difficult to conduct consistently and sys-tematically. Therefore, Natural Language Processing (NLP) approaches are required to automati-cally identify sentiment tendencies within student comments. This study aims to compare the per-formance of Gaussian Naive Bayes and Support Vector Machine (SVM) algorithms for classifying sentiment in SET comments using IndoBERT-based text embeddings. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, including stages of data understanding, data preparation, modeling, evaluation, and deployment. Text comments were preprocessed and transformed into numerical vectors using IndoBERT Sentence-BERT em-beddings to capture contextual semantic relationships between words. These embeddings were then used as input features for both classification algorithms. Evaluation results show that the In-doBERT + SVM model achieved an accuracy of 93.88%, outperforming IndoBERT + Naive Bayes which obtained 92.40%. The SVM model also demonstrated more balanced precision, recall, and F1-score values across sentiment classes. These findings indicate that SVM is more effective in uti-lizing high-dimensional contextual embeddings for sentiment classification of student feedback.
Penerapan Kerangka CRISP-DM dalam Evaluasi Performa Logistic Regression dan Decision Tree untuk Prediksi Kelulusan Mahasiswa Nabarian, Tifanny; Wahyuni, Imelda; Farisi, Salman El
Indonesian Journal Computer Science Vol. 5 No. 1 (2026): April 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v5i1.12502

Abstract

Ketepatan waktu kelulusan mahasiswa sering digunakan sebagai salah satu indikator dalam menilai efektivitas penyelenggaraan pendidikan di perguruan tinggi. Pemanfaatan data akademik melalui pendekatan machine learning dapat membantu mengidentifikasi mahasiswa yang berpotensi mengalami keterlambatan kelulusan secara lebih terukur. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Logistic Regression dan Decision Tree dalam memprediksi kelulusan mahasiswa berdasarkan faktor akademik. Dataset yang digunakan terdiri dari 351 mahasiswa Program Studi Teknik Informatika di STT Terpadu Nurul Fikri, angkatan 2021, dengan variabel input Indeks Prestasi Kumulatif (IPK) serta Indeks Prestasi Semester (IPS) untuk setiap semester. Penelitian ini menggunakan metodologi CRISP-DM dengan pembagian data pelatihan serta pengujian 80:20. Evaluasi model dilakukan menggunakan confusion matrix dengan metrik accuracy, precision, recall, serta F1-score. Hasil pengujian menunjukkan bahwa Logistic Regression memperoleh accuracy 92,96%, precision 90,91%, recall 97,56%, dan F1-score 94,12%, sedangkan Decision Tree memperoleh accuracy 88,73%, precision 88,37%, recall 92,68%, serta F1-score 90,48%. Hasil tersebut menunjukkan bahwa Logistic Regression menunjukkan performa yang lebih optimal dan lebih sesuai dalam memodelkan hubungan antara variabel akademik dan ketepatan kelulusan mahasiswa. Sebagai kontribusi utama, penelitian ini mengintegrasikan model Logistic Regression terbaik ke dalam purwarupa sistem deteksi dini (early warning system) berbasis web menggunakan Streamlit. Pendekatan ini memberikan keunggulan praktis bagi pemangku kepentingan program studi untuk memitigasi risiko keterlambatan kelulusan secara real-time dan mendukung pengambilan keputusan intervensi akademik yang sepenuhnya digerakkan oleh data (data-drive decision making).
Implementasi Agile Scrum dalam Pengembangan Sistem Perpustakaan Digital Sekolah SMAN 47 Jakarta Selatan Domami Pandu Saputra; Tifanny Nabarian; Efrizal Zaida
DBESTI: Journal of Digital Business and Technology Innovation Vol 3 No 1 (2026): Mei, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v3i1.2210

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem perpustakaan digital berbasis web pada localhost only dengan menerapkan metode Agile Scrum pada studi kasus SMAN 47 Jakarta Selatan. Pendekatan yang digunakan dalam penelitian ini adalah pendekatan kualitatif dengan karakteristik subjek siswa dan pustakawan sebagai pengguna utama sistem. Pengumpulan data dilakukan melalui wawancara semi-terstruktur, observasi langsung, serta pengujian dilakukan dengan menggunakan metode black-box testing. Proses pengembangan sistem dilakukan dengan metode Agile Scrum yang mencakup tahapan product backlog, sprint planning, daily scrum, sprint review, dan sprint retrospective. Hasil pengujian menunjukkan bahwa fitur utama seperti login, registrasi, pencarian buku, meminjam buku, mengembalikan buku, daftar favorit, riwayat peminjaman, dashboard admin, dan kelola data buku/koleksi telah berfungsi sesuai kebutuhan pengguna. Evaluasi dari umpan balik pengguna menunjukkan bahwa sistem cukup mudah digunakan, responsif, dan mampu mempermudah layanan perpustakaan. Hasil dari penelitian ini menyimpulkan bahwa metode Agile Scrum efektif diterapkan dalam pengembangan sistem perpustakaan digital yang berbasis kebutuhan pengguna di lingkungan sekolah. Selanjutnya pengembangan dapat dilanjutkan dengan penambahan fitur seperti notifikasi pengembalian, deploy publik dan akses ke mobile untuk meningkatkan jangkauan layanan.  
Penyediaan Platform Penjadwalan Kegiatan Olahraga Masyarakat Kota Depok Tifanny Nabarian; Maryam Hasnaa’ Syamila; St. Nurmuhsina; Laisa Nurin Mentari; Betty Amalia
Dedikasi Sains dan Teknologi (DST) Vol. 5 No. 2 (2025): Artikel Pengabdian Nopember 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v5i2.7132

Abstract

Banyaknya Induk Organisasi Olahraga (INORGA) yang dinaungi Komite Olahraga Masyarakat Indonesia (KORMI) Kota Depok menyebabkan para anggota kesulitan untuk mengetahui jadwal dari setiap acara olahraga rekreasi yang dinaungi oleh KORMI Kota Depok. Dengan demikian, informasi terkait aktivitas olahraga rekreasi ini juga jarang untuk diketahui oleh masyarakat luas. Padahal, tugas dan tanggung jawab utama KORMI adalah untuk meningkatkan kesadaran masyarakat dalam berkegiatan olahraga dan kebugaran. Untuk mendukung tugas utama tersebut, pengurus KORMI Kota Depok membutuhkan suatu platform yang dapat mewadahi data kegiatan dan peserta kegiatan olahraga rekreasi ini, khususnya di Kota Depok. Kegiatan pengabdian ini bertujuan untuk menghasilkan Sistem Informasi atau Platform Kegiatan Olahraga Rekreasi yang dapat digunakan oleh seluruh INORGA di bawah naungan KORMI Kota Depok untuk menyebarkan informasi kegiatan olahraga yang bermanfaat untuk masyarakat. Penyediaan solusi berbasis Teknologi Informasi ini dilakukan dengan bantuan diagram UML untuk perancangannya. Hasil dari kegiatan ini terdapat peningkatan signifikan sebesar 2.32 poin dari hasil penyelenggaran postest setelah kegiatan bimbingan teknis dilaksanakan.
Evaluasi Performa Algoritma Klasterisasi dalam Mengelompokkan Topik Tugas Akhir: Studi Komparasi K-Means dan DBSCAN Tifanny Nabarian; Dhea Marsella; Salman El Farisi
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol. 15 No. 3 (2026): Juni 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i3.3759

Abstract

The increasing number of Informatics Engineering students at STT-NF has resulted in a growing volume of final project topics that need to be efficiently categorized into research areas. This study evaluates the performance of the K-Means and DBSCAN algorithms for clustering students' final project topics based on their textual characteristics. The research follows the CRISP-DM framework and utilizes a dataset of 656 final project titles. The data were processed through text preprocessing, TF-IDF weighting, and dimensionality reduction PCA. The experimental results indicate that K-Means with k = 13 achieved the best performance, obtaining a silhouette score of 0.1434 and a purity score of 0.8659, outperforming DBSCAN, which achieved a silhouette score of 0.1153 and a purity score of 0.6589. The resulting clusters were successfully mapped into four major research areas, namely Software Engineering, Network Engineering & Cyber Security, Data Engineering, and UI/UX Design. Furthermore, the best-performing model was implemented in an interactive Streamlit-based dashboard to support research area mapping and academic supervisor assignment. Keywords: K-Means; DBSCAN; Final Project Topic Clustering; TF-IDF   Abstrak Peningkatan jumlah mahasiswa Teknik Informatika di STT-NF menghasilkan semakin banyak data topik tugas akhir yang perlu dikelompokkan ke dalam bidang penelitian secara efisien. Penelitian ini mengevaluasi performa algoritma K-Means dan DBSCAN untuk mengelompokkan topik tugas akhir mahasiswa berdasarkan kemiripan karakteristiknya. Proses penelitian mengikuti metodologi CRISP-DM dengan menggunakan 656 judul tugas akhir yang diproses melalui tahapan pemrosesan teks, pembobotan TF-IDF, dan reduksi dimensi PCA. Berdasarkan hasil pengujian, K-Means dengan k = 13 memberikan kinerja terbaik dengan nilai silhouette score 0.1434 dan purity 0.8659, sedangkan DBSCAN memperoleh nilai silhouette score 0.1153 dan purity 0.6589. Klaster yang terbentuk berhasil direpresentasikan ke dalam empat bidang penelitian utama, yaitu Software Engineering, Network Engineering & Cyber Security, Data Engineering, dan UI/UX Design. Sebagai implementasi, model terbaik diintegrasikan ke dalam dashboard interaktif berbasis Streamlit untuk mendukung pemetaan bidang penelitian dan penentuan dosen pembimbing. Kata kunci: K-Means; DBSCAN; Klasterisasi Topik Tugas Akhir; TF-IDF
Analisis Perbandingan Kinerja IndoBERT dan TF-IDF dalam Mengklasifikasikan Sentimen EDOM Menggunakan Algoritma K-Nearest Neighbor: Comparative Analysis of IndoBERT and TF-IDF Performance in Classifying EDOM Sentiments Using the K-Nearest Neighbor Algorithm Tifanny Nabarian; Siti Nurhalizah; Salman El Farisi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2614

Abstract

Dalam konteks pendidikan tinggi, dosen berkontribusi besar terhadap peningkatan kualitas pembelajaran. Di Sekolah Tinggi Teknologi Terpadu Nurul Fikri (STT NF), Evaluasi Dosen oleh Mahasiswa (EDOM) dilaksanakan setiap akhir semester dan menghasilkan data komentar mahasiswa. Namun, analisis masih dilakukan secara manual sehingga kurang efisien dan berpotensi subjektif. Selain itu, belum terdapat kajian komparatif mengenai metode representasi yang sesuai untuk digunakan bersama algoritma K-Nearest Neighbor (KNN) khususnya pada data EDOM. Tujuan dari penelitian ini adalah menganalisis dan membandingkan kinerja IndoBERT dan TF-IDF dalam merepresentasikan teks untuk klasifikasi sentimen komentar EDOM menggunakan KNN. Metode penelitian mengacu pada tahapan CRISP-DM dengan dataset komentar EDOM tahun 2024. Hasil penelitian menunjukkan bahwa IndoBERT+KNN menghasilkan accuracy sebesar 0,903 serta menunjukkan nilai precision, recall, dan F1-score yang lebih seimbang antarkelas dibandingkan TF-IDF+KNN yang memperoleh accuracy sebesar 0,820 dengan performa metrik evaluasi yang cenderung kurang seimbang antarkelas. Hasil ini menunjukkan representasi kontekstual IndoBERT lebih efektif dalam menangani kompleksitas komentar EDOM dan algoritma KNN yang berbasis jarak dibandingkan dengan pendekatan berbasis frekuensi kata pada TF-IDF. Berdasarkan temuan tersebut, penelitian ini memberikan pemilihan metode representasi teks yang lebih optimal untuk pengembangan analisis sentimen secara lebih objektif dan efisien
Co-Authors Afifah, Raihana Cindy Ali Akbar Ananto Dwi Saputro Aris Ganiardi, Muhammad Asqia, Misna Betty Amalia Betty Amalia Davied Wahyu Wismanindra Dhea Marsella Domami Pandu Saputra Dymas Sutrysno Efrizal Zaida Firsandaya Malik, Reza Imelda Wahyuni, Imelda Lukman Rosyidi Maryam Hasnaa' Syamila Maryam Hasnaa’ Syamila Mentari, Laisa Nurin Muhammad Aris Ganiardi Nuranisah, Nuranisah Nurmuhsina, St. Putri, Dea Amelia Rahmatul Zakia Ramadhan, Muhammad Sayyid Ramdani, Muhammad Asnur Reza Firsandaya Malik Reza Maulana Salman Al Farisi Salman El Farisi Sella Rabila Sirojul Munir Sirojul Munir Siti Nurhalizah Slamet Santoso St. Nurmuhsina Syamila, Maryam Hasnaa' Waluyo, Ahmad