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All Journal TEKNIK INFORMATIKA Jurnal Simetris JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA Prosiding SNATIF NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JURNAL MANAJEMEN (EDISI ELEKTRONIK) Shirkah: Journal of Economics and Business Simtek : Jurnal Sistem Informasi dan Teknik Komputer STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Elektronik Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] G-Tech : Jurnal Teknologi Terapan JUKI : Jurnal Komputer dan Informatika Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA Infotech: Journal of Technology Information Riset Pendidikan Bahasa dan Sastra Indonesia (Repetisi) International Journal of Community Service Simpatik: Jurnal sistem Informasi dan Informatika Buletin Sistem Informasi dan Teknologi Islam Journal of Management and Digital Business Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Duta Abdimas: Jurnal Pengabdian Masyarakat Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Innovative: Journal Of Social Science Research Nusantara Journal of Computers and its Applications Jurnal INFOTEL SmartComp CSRID Jurnal Sosialita: Jurnal Kajian Sosial dan Pendidikan Journal of Information Technology RESWARA: Jurnal Riset Ilmu Teknik Jurnal Teknik Informatika dan Teknologi Informasi
Nurmalitasari Nurmalitasari
Universitas Duta Bangsa Surakarta
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Hybrid Logistic Super Newton Model for Predicting Small Sample Size Data Nurmalitasari, Nurmalitasari; Awang Long, Zalizah; Nurchim, Nurchim
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.43929

Abstract

Logistic regression is a model commonly used for predicting data with large sample sizes. However, in real-world scenarios, many cases involve small datasets that need to be addressed using logistic regression. The aim of this research is to develop a hybrid logistic regression model to address issues with small sample sizes by combining the Newton Raphson and Super Cubic methods. This hybrid model is applied to predict student dropout at Universitas Duta Bangsa Surakarta. The performance of the hybrid model is evaluated using two main metrics: the convergence of the parameter approximation to measure the precision of parameter estimation, and the ROC curve to assess prediction accuracy. Experimental results show that the Hybrid Logistic Super Newton model outperforms the logistic regression Newton Raphson model, requiring only three iterations to converge, thus improving computational efficiency. Moreover, this model achieves higher accuracy, with an AUC of 0.8833. These findings suggest that the developed model has the potential to be applied in various fields, such as healthcare, finance, and others, offering an effective solution for accurate, real-time predictive analytics. Further research could focus on optimizing the model’s computational efficiency and exploring its application in other domains with small dataset challenges, such as healthcare and finance.
PERUBAHAN SOSIAL SEDULUR SIKEP DI DUKUH KARANGPACE DESA KLOPODUWUR KECAMATAN BANJAREJO KABUPATEN BLORA PADA ERA MODERN NURMALITASARI, NURMALITASARI
Jurnal Sosialita Vol. 16 No. 2 (2021): JURNAL SOSIALITA
Publisher : Program Magister Pendidikan IPS UPY

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan untuk mengetahui : 1) sejarah munculnya Sedulur Sikep di dukuh Karangpace; 2) Perubahan sosial Sedulur Sikep di dukuh Karangpace pada era modern; 3) Nilai-nilai kearifan lokal Sedulur Sikep untuk dijadikan literasi dalam pengembangan pembelajaran IPS di sekolah.Penelitian ini merupakan penelitian kualitatif. Subyek dalam penelitian ini adalah Sedulur Sikep yang tingal di dukuh Karangpace. Pengumpulan data dilakukan dengan wawancara, observasi, dan dokumen. Hasil penelitian ini menunjukkan: 1) Sedulur Sikep lahir dari sejarah seorang tokoh yaitu Samin Surosentiko yang mampu mengerakan massa untuk melawan kolonialisme Belanda. Dari gerakan ini masyarakat pengikut Samin semakin banyak dan meluas hingga ke berbagai daerah termasuk ke Jawa Timur. Masyarakat ini kemudian menyebut dirinya Sedulur Sikep. Artinya: Sikap menikah/ berumah tangga.2) terdapat perubahan sosial yang terjadi mencakup beberapa bidang yakni: Ilmu pengetahuan dan pendidikan, teknologi dan transportasi, agama dan kepercayaan, tradisi dan adat istiadat, dan moral ekonomi. 3) Nilai-nilai kearifan lokal dapat dijadikan salah satu literasi dalam pengembangan pembelajaran IPS di sekolah.Kata Kunci : Perubahan sosial, Sedulur Sikep, kearifan lokal, modernisasi.This study aims to find out: 1) the history of the emergence of Sedulur Sikep in the Karangpace hamlet; 2) Sedulur Sikep social change in the Karangpace hamlet in the modern era; 3) Sedulur Sikep local wisdom values in the Karangpace hamlet to be used as literacy in the development of social studies in schools. This research is a qualitative research. The subjects in this study were Sedulur Sikep who lived in the Karangpace hamlet. The data collections were carried out by interviews, observation and document. The results of this study indicate: 1) Sedulur Sikep was born from history of a figure namely Samin Surosentiko who was able to mobilize the masses to fight the Dutch colonialism. From this movement the followers of Samin became more numerous and extended to various regions including East Java. This community then call themself as Sedulur Sikep or Sikep Rabi. Meaning: The attitude of marriag, 2) There were social changes that occur in Sedulur Sikep which cover several fields, namely: Science and education, technology and transportation, religion and beliefs, traditions and customs, and economic morals. 3) The values of local wisdom can be used as literacy in the development of social studies learning in schools.Keywords: Social change, Sedulur Sikep, local wisdom, modernization.
Prediksi Churn Pelanggan Multinational Bank Menggunakan Algoritma Machine Learning Hidayat, Rifki; Syawaludin, M Ainur; Nurmalitasari, Nurmalitasari
Simpatik: Jurnal Sistem Informasi dan Informatika Vol. 4 No. 2 (2024): Desember 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/simpatik.v4i2.4595

Abstract

Dalam menghadapi persaingan pasar yang ketat, prediksi churn pelanggan menjadi penting bagi perusahaan perbankan untuk mempertahankan loyalitas pelanggan. Penelitian ini mengaplikasikan algoritma machine learning meliputi Naive Bayes, Decision Tree, dan Random Forest untuk prediksi churn pelanggan pada ABC Multinational Bank. Data yang digunakan adalah dataset publik yang diambil dari Kaggle yang mencakup informasi 10.000 nasabah bank. Proses penelitian melibatkan beberapa tahapan yaitu pengumpulan data, preprocessing, pemodelan, prediksi, dan evaluasi. Hasil evaluasi memperlihatkan bahwa model Random Forest memberikan performa terbaik dengan akurasi 85% dan AUC 0.83. Naive Bayes dan Decision Tree masing-masing memiliki akurasi 82% dan 77%. Kesimpulan menunjukkan bahwa Random Forest lebih unggul dalam memprediksi churn pelanggan dibandingkan dua algoritma lainnya, sehingga dapat digunakan untuk strategi pemasaran yang lebih efektif dalam industri perbankan.
Implementasi NLP untuk Deteksi Teks Buatan AI (Chat-GPT) menggunakan Metode Naive Bayes Rafel Fernando; Yuliana Dewi Proboningrum; Septi Dwi Supriati; Nurmalitasari Nurmalitasari
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2026

Abstract

The development of artificial intelligence (AI) technology, especially large language models like ChatGPT, presents challenges related to the authenticity and validity of digital content. AI's ability to produce human-like text opens up opportunities for misuse, such as plagiarism and information manipulation. This study aims to develop an AI text detection system using the Multinomial Naive Bayes algorithm, due to its ease of use and high effectiveness algorithm has become a popular choice for text classification.. The dataset used is the Human ChatGPT Comparison Corpus (H3C), sourced from the ELI5 subreddit on Reddit, consisting of 800 entries of questions and answers from both humans and AI. The labeling process involves combining answers into a single column and assigning labels based on the source. Preprocessing steps include case folding, removal of digits and punctuation, tokenization, stopword removal, normalization, and text finalization. Text features are extracted using the TF-IDF method, limited to the top 1000 features. The model is trained on 80% of the data and tested on the remaining 20%. The evaluation shows an accuracy of 93%. These findings suggest that the Naive Bayes method is effective in distinguishing AI-generated from human-generated text and has potential as an automatic AI content detection tool.
Traffic Accident Severity Classification System Using Random Forest Algorithm Ega Muhammad Atsir; Nurmalitasari Nurmalitasari; Aprilisa Arum Sari
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2089

Abstract

Traffic accidents pose a major concern in many countries, including Indonesia, causing considerable losses, injuries, and fatalities each year. Properly classifying the severity of these incidents is essential for authorities to establish preventive actions, apply effective countermeasures, and improve overall road safety. Conventional statistical techniques often fall short in capturing the intricate relationships among multiple influencing variables, such as weather, driver experience, vehicle type, number of vehicles, and casualty figures. To address this limitation, this study proposes a machine learning–based classification method using the Random Forest algorithm, known for its robustness in handling complex and high-dimensional data while identifying nonlinear patterns. The model was trained on a traffic accident dataset from Kaggle and incorporated important features, including driver age group, driving experience, type of vehicle, lighting and weather conditions, type of collision, number of vehicles involved, and casualties. The proposed system achieved 81% accuracy, 75% weighted precision, 81% weighted recall, and a weighted F1-score of 77%, demonstrating reliable performance in predicting accident severity levels Slight Injury, Serious Injury, and Fatal Injury. And providing useful insights for data-driven planning in traffic safety management.
Analisis Prediksi Penjualan Isi Ulang Air Galon menggunakan Metode LSTM dan SARIMA Ulfarida Miftakhul Jannah; Nurmalitasari Nurmalitasari; Ridwan Dwi Irawan
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.802

Abstract

Refillable drinking water depots often face challenges in dealing with unpredictable customer demand on a daily basis. This uncertainty complicates the process of stock management, production planning, and overall operations. Without accurate sales forecasts, depots risk losing potential sales and experiencing a decline in service quality to customers. Therefore, a solution is needed that can accurately predict daily sales. The first step in this research is to collect relevant data. Once the data is available, pre-processing is conducted to prepare the data before entering the modeling process. The Long Short-Term Memory (LSTM) model has the advantage of remembering historical patterns in time series data. Meanwhile, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model is an extension of ARIMA that can handle data with seasonal characteristics. In this study, the LSTM model demonstrated better performance than SARIMA. This is evidenced by the performance evaluation values: MAPE of 9.54%, RMSE of 0.17, and MAE of 0.14 for the LSTM model, which are lower than MAPE of 10.51%, RMSE of 0.19, and MAE of 0.16 for SARIMA. These values indicate that LSTM is capable of providing more accurate prediction results. Based on these results, it can be concluded that the LSTM model is more effective and recommended for use in predicting daily sales of refillable water at the Manshurin Water depot
Analisis Sentimen Opini Publik pada Channel Youtube Mata Najwa Menggunakan Metode SVM Asmara Andhini; Fadilah Nuria Handayani; Intan Diasih; Nurmalitasari
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5426

Abstract

The rapid development of social media, particularly the YouTube platform, has created an active and open space for public discourse. One prominent example is the program "Mata Najwa", which frequently discusses important societal issues. The episode titled "Retno Marsudi & Sri Mulyani: Women in Power Mata Najwa" garnered significant attention, sparking a variety of responses from netizens in the comments section. This study aims to explore public sentiment toward female leadership by utilizing the Support Vector Machine (SVM) classification method. A total of 4,626 comments from Najwa Shihab’s YouTube channel on the aforementioned episode were analyzed through several stages, including data preprocessing, sentiment labeling using a lexicon-based approach, feature extraction via the TF-IDF method, and classification using the SVM algorithm. The model evaluation demonstrated excellent performance, with an accuracy of 95.36%, precision of 95.70%, recall of 95.36%, and an F1-score of 95.27%. The model accurately identified positive and neutral comments but showed a limitation in detecting negative comments, likely due to class imbalance. This study offers new insights into public perceptions in digital spaces and reaffirms the effectiveness of SVM in text-based sentiment analysis.
ANALISIS SENTIMEN DAN RINGKASAN ULASAN APLIKASI ACCESS BY KAI MENGGUNAKAN NUSABERT DAN LLM QWEN3 Muhammad Ilham Arsyam; Pipin Widyaningsih; Nurmalitasari
Jurnal Informatika dan Rekayasa Elektronik Vol. 9 No. 1 (2026): JIRE April 2026
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v9i1.2001

Abstract

Dalam era digital saat ini, ulasan pengguna di platform aplikasi menjadi sumber informasi berharga bagi pengembang untuk meningkatkan kualitas layanan. Analisis sentimen dari ulasan tersebut memerlukan pendekatan yang efektif dan akurat, terutama dalam konteks bahasa Indonesia. Penelitian ini mengusulkan sistem analisis sentimen dan ringkasan ulasan aplikasi Access by KAI menggunakan teknologi pemrosesan bahasa alami terkini. Metodologi yang diterapkan mengikuti tahapan CRISP-DM, dimulai dari pengumpulan data ulasan melalui web scraping Google Play Store, preprocessing teks, hingga pengembangan model klasifikasi dan ringkasan. Model NusaBERT digunakan untuk analisis sentimen dengan pembagian data 70% latih, 15% validasi, dan 15% uji, sementara model Qwen3-8B dimanfaatkan untuk menghasilkan ringkasan abstraktif yang terstruktur. Sistem yang dikembangkan dibandingkan dengan pendekatan tradisional menggunakan SVM dengan ekstraksi fitur TF-IDF. Hasil evaluasi menunjukkan bahwa NusaBERT mencapai akurasi 90.8% dan F1-score 81.55%, mengungguli model SVM yang mencapai akurasi 88.8% dan F1-score 55.33%. Sistem ini berhasil mengidentifikasi pola sentimen pengguna secara akurat serta menghasilkan ringkasan yang komprehensif dalam format enam kategori utama. Antarmuka berbasis Streamlit yang dikembangkan memungkinkan visualisasi data yang intuitif melalui pie chart, bar chart, confusion matrix, dan wordcloud. Penelitian ini membuktikan efektivitas model berbasis transformer dalam memahami konteks bahasa Indonesia untuk analisis sentimen, sekaligus memberikan solusi praktis bagi pengembang aplikasi dalam memahami umpan balik pengguna secara efisien. Hasil penelitian ini dapat dimanfaatkan oleh pengembang aplikasi sebagai pendukung pengambilan keputusan berbasis data dalam meningkatkan kualitas layanan dan pengalaman pengguna.
SISTEM REKOMENDASI PEMILIHAN PAKET INTERNET WIFI MENGGUNAKAN KNOWLEDGE BASED RECOMMENDATION Ardean Risqi Laksono; Nurmalitasari Nurmalitasari; Ridwan Dwi Irawan
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.437

Abstract

The internet has become a crucial need in modern society, including in rural areas such as Wonokarto. PT. Yasmin Amanah Media, as a local internet service provider, offers a variety of internet packages to meet customer needs. However, the wide range of options often leads to confusion in selecting the most suitable package. This research aims to design a WiFi internet package recommendation system based on Knowledge-Based Recommendation (KBR) that can provide personalized suggestions based on user preferences. The system development method used is the System Development Life Cycle (SDLC), which consists of planning, analysis, design, implementation, testing, and maintenance phases. This study focuses on the system design stage in the form of a web-based application. Data were collected through interviews, observation, and literature study. The designed recommendation system utilizes constraint-based techniques to match user requirement attributes with the characteristics of available internet packages. The results show that the knowledge-based recommendation approach is effective in providing internet package selection suggestions, especially for new users who have no previous interaction history. This system is expected to improve customer satisfaction, internet usage efficiency, and loyalty to the services of PT. Yasmin Amanah Media.
Penerapan Algoritma Decision Tree Untuk Prediksi Tingkat Risiko Jentik Nyamuk Berdasarkan Data Pemeriksaan Posyandu Aines Nafis Husna; Nurmalitasari Nurmalitasari; Afu Ichsan Pradana
JUKI : Jurnal Komputer dan Informatika Vol. 8 No. 1 (2026): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v8i1.2449

Abstract

Pemantauan jentik nyamuk merupakan kegiatan yang dilakukan untuk mengetahui kondisi lingkungan dan mencegah peningkatan populasi nyamuk yang berpotensi menimbulkan penyakit. Penentuan tingkat risiko wilayah berdasarkan hasil pemeriksaan jentik masih dilakukan secara manual sehingga berpotensi menimbulkan perbedaan penilaian. Penelitian ini bertujuan untuk membangun model klasifikasi tingkat risiko jentik nyamuk menggunakan algoritma Decision Tree berdasarkan data historis pemeriksaan jentik nyamuk. Data yang digunakan merupakan hasil rekapitulasi pemeriksaan jentik nyamuk di Desa Langenharjo, Kecamatan Grogol, periode 2023-2025 sebanyak 65 data. Variabel yang digunakan meliputi jumlah rumah diperiksa, jumlah rumah terdapat jentik, jumlah kontainer diperiksa, dan jumlah kontainer terdapat jentik. Tahapan penelitian meliputi pengumpulan data, preprocessing, pelabelan tingkat risiko, pembentukan model Decision Tree, serta pengujian menggunakan pembagian data sebesar 80% sebagai data latih dan 20% sebagai data uji. Hasil penelitian menunjukkan bahwa model mampu mengklasifikasikan tingkat risiko jentik nyamuk ke dalam kategori rendah, sedang, dan tinggi dengan tingkat akurasi sebesar 92%. Hasil klasifikasi tersebut kemudian diimplementasikan pada aplikasi berbasis web untuk membantu proses input data dan penyajian informasi. Berdasarkan hasil tersebut, algoritma Decision Tree dapat digunakan sebagai alat bantu dalam menentukan tingkat risiko jentik nyamuk secara lebih objektif serta mendukung pengambilan keputusan dalam kegiatan pemantauan lingkungan.
Co-Authors AA Sudharmawan, AA Abdullah Sajad Adenta Sari, Nur Avia Aditya Gema Pratama Afu Ichsan Pradana Agus Mardiyono Ahmad Tomi Aines Nafis Husna Alliya, Sania Qorrii Aprilisa Arum Sari Ardean Risqi Laksono Ashary Vermaysha Asmara Andhini Awang Long, Zalizah Bangun Prajadi Cipto Utomo Bima Setya Gumelar Dwi Hartanti Dwiki Rasya Rahadian Ega Muhammad Atsir Eko Purwanto Fadilah Nuria Handayani FAULINDA ELY NASTITI Hapsari, Theresia Pinaka Ratna Ning Hasanah, Herliyani Imam Sujadi Intan Diasih Irawan, Ridwan Dwi Irfan Nur Sofiyanto JOKO SUSANTO Lindha Juniarta Suseno Lucky Marsella Miftahul Hasanah Miftakhul Jannah, Ulfarida Moh Muhtarom Mohammad Yusuf Nugroho Muhammad Hafizh Al Mustofa Muhammad Ilham 'Aziiz Alfarobi Muhammad Ilham Arsyam Muhammad Rais Ramadhani Novemy Triyandari Nugroho Nugroho Arif Sudibyo Nugroho, Mohammad Yusuf Nurchim Permatasari, Hanifah Pipin Widyaningsih Rafel Fernando Rahmawati Desi Tri Wulandari Ratna Puspita Indah Rico Yoga Pradana Rifki Hidayat Rizqi Saputra Rudi Susanto Sajad, Abdullah Sari, Nur Avia Adenta Septi Dwi Supriati Setiawati, Neha Poetri Singgih Purnomo Sofa Marwati SRI SUMARLINDA Sulami, Atik Syaiful Nur Wardani Syawaludin, M Ainur Tariq Taufik Hidayat Tri Atmojo Kusmayadi Tri Wulandari, Rahmawati Desi Ulfarida Miftakhul Jannah Utami, Maulinda Devi Vihi Atina Viona Putri Ardiana Widya Monika Sari Yuliana Dewi Proboningrum Yusmawan Dwi Suseno Zalizah Awang Long Zalizah Awang Long