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All Journal TEKNIK INFORMATIKA Jurnal Simetris JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] G-Tech : Jurnal Teknologi Terapan Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Nusantara Journal of Computers and its Applications Jurnal INFOTEL CSRID Journal of Information Technology Jurnal Teknik Informatika dan Teknologi Informasi
Nurmalitasari Nurmalitasari
Universitas Duta Bangsa Surakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Other

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Penerapan Regresi Linier Berganda Untuk Memprediksi Harga Laptop Dengan Menggunakan Software Python Syaiful Nur Wardani; Nurmalitasari Nurmalitasari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2023
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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Abstract

Pada era revolusi industri 4.0 saat ini perkembangan produk elektronik dan digital semakin maju pesat dengan berkembangnya suatu produk elektronik terutama laptop yang mempunyai berbagai macam-macam varian dan komponen penyusun yang sangat berkualitas membuat masyarakat ingin membelinya. Data yang digunakan dalam penelitian ini adalah data yang diperoleh dari website ELS Computer yang berjumlah 65 data. Metode yang digunakan dalam penelitian ini adalah Regresi linier. Regresi liniear adalah metode yang memodelkan variabel dependen dan variabel independen dengan menganalisis hubungan antar variabel-variabel tersebut. Penelitian ini menggunakan variabel dependennya adalah harga dengan variabel independennya adalah ukuran layar, RAM Storage, dan berat laptop. Tujuan dari penelitian ini adalah memprediksi harga laptop menggunakan metode regresi linier. Prediksi hasil evaluasi linier dalam nilai MAE,MSE,RMSE,dan MAPE menghasilkan nilai keakuratan prediksi sebesar 0.34.
Memprediksi Kenaikan Jumlah Penumpang Kereta Api Menggunakan Metode Regresi Linier Dengan Pemrograman Python Lucky Marsella; Nurmalitasari Nurmalitasari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2023
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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Abstract

Penggunaan angkutan umum sangat bermanfaat bagi pemerintah untuk mengatasi masalah kemacetan lalu lintas yang merupakan masalah umum di Indonesia. Contoh angkutan umum yang disukai penduduk adalah kereta api. Tujuan dari penelitian ini adalah memprediksi laju pertumbuhan jumlah penumpang kereta api. Penelitian ini juga menggunakan metode regresi linier dengan menggunakan bahasa pemrograman Python. Data yang diambil dari website Badan Pusat Statistika (BPS) pada tahun 2022 mulai bulan Januari hingga Desember.
Perancangan Sistem Rekomendasi Tingkat Mortalitas Ibu Hamil Menggunakan Algoritma MOORA Bima Setya Gumelar; Moh Muhtarom; Nurmalitasari Nurmalitasari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2023
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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Abstract

Mortalitas merujuk pada jumlah kematian dalam suatu populasi atau kelompok dalam jangka waktu tertentu. WHO juga membedakan antara mortalitas umum (general mortality), yaitu jumlah kematian secara keseluruhan dalam populasi, dengan mortalitas spesifik (specific mortality), yaitu jumlah kematian yang disebabkan oleh penyakit atau kondisi tertentu. Metode yang digunakan adalah metode MOORA yang merupakan salah satu metode dari sistem pendukung keputusan. Melalui penerapan metode ini, peneliti akan membandingkan setiap ibu hamil berdasarkan kriteria yang akan menentukan rekomendasi kepada ibu hamil. Hasil dari penelitian ini mendapatkan hasil nilai tertinggi yaitu 7,556
Analisis Peserta Program Keluarga Berencana KB Aktif Menggunakan Metode Regresi Linear Di Kelurahan Combongan Sukoharjo Lindha Juniarta Suseno; Nurmalitasari Nurmalitasari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2023
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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Abstract

Penelitian ini bertujuan untuk menganalisis faktor-faktor yang berpengaruh terhadap jumlah peserta Program Keluarga Berencana (KB) aktif di Kelurahan Combongan, Sukoharjo menggunakan metode regresi linear. Data yang digunakan meliputi variabel independen seperti Kepala Keluarga (KK), Pasangan Usia Subur (PUS), Menopouse, Metoda Operasi Wanita (MOW), Medis Operasi Pria (MOP), Pembinaan Kader Institusi masyarakat Pedesaan (IMP) dan variabel dependen yaitu Perkiraan Permintaan Masyarakat(PPM) status keaktifan peserta KB (aktif atau tidak aktif). Penelitian ini menggunakan desain penelitian cross-sectional dengan sampel sebanyak 200 responden peserta KB yang dipilih secara purposive. Data dikumpulkan melalui kuesioner dan dianalisis menggunakan regresi linear untuk mengetahui hubungan antara variabel independen dan dependen. Hasil penelitian ini diharapkan dapat memberikan pemahaman yang lebih baik tentang faktor-faktor yang mempengaruhi jumlah peserta KB aktif di Kelurahan Combongan, Sukoharjo, serta memberikan sumbangan informasi bagi perencanaan dan pengembangan Program KB di tingkat lokal.
Analisis Sentimen Model Distilbert Multilingual Cased Dalam Mengklasifikasikan Ulasan Game Genshin Impact Abdullah Sajad; Nurmalitasari Nurmalitasari; Eko Purwanto
Computer Science Research and Its Development Journal Vol. 16 No. 2 (2024): June 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid.16.2.2024.124-136

Abstract

The evolution of information technology has revolutionized how humans engage with the world, particularly within the gaming sector. This paper explores the utilization of the DistilBERT Multilingual Cased model for analyzing sentiments expressed in Genshin Impact game reviews. The research methodology encompasses gathering data from Google PlayStore and Apple AppStore, manually labeling data, preprocessing it, and employing the DistilBERT Multilingual Cased model for analysis. The model's performance is assessed using metrics such as accuracy, precision, recall, and f1-score. Findings reveal that the model effectively categorizes sentiment in reviews, achieving an overall accuracy of 82%. Precision, recall, and f1-score metrics consistently surpass 0.77 across all sentiment categories. This study concludes that the DistilBERT Multilingual Cased model shows promise as a valuable tool for multilingual sentiment analysis within the realm of game reviews.
Sistem Rekomendasi Pemilihan Basic Skincare Menggunakan Metode Content Based Filtering Viona Putri Ardiana; Vihi Atina; Nurmalitasari Nurmalitasari
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.4966

Abstract

Kumara Store offers a diverse range of cosmetics and skincare products in Mandan, Sukoharjo. Due to the extensive variety of products, customers frequently find it challenging to select basic skincare items. To address this issue, developing a recommendation system is crucial to assist customers in finding the most suitable products. This study employs the Content-Based Filtering method alongside the Rapid Application Development approach. The system features include product search and viewing capabilities, with recommendations being generated for users. Admin features allow for the management of attributes and data related to basic skincare products. The Content-Based Filtering technique recommends similar products based on their features or attributes. With 35 product samples and 6 attributes examined, the product Emina MS Pimple achieves the highest similarity score of 0.694 and will be recommended as a basic skincare product. This research can provide a foundation for future studies aimed at enhancing accuracy.
Penerapan Metode Content Based Filtering dalam Sistem Rekomendasi Pemilihan Produk Skincare Sulami, Atik; Atina, Vihi; Nurmalitasari, Nurmalitasari
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 9, No 2 (2024)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v9i2.24066

Abstract

Hybrid Logistic Super Newton Model for Predicting Small Sample Size Data Nurmalitasari Nurmalitasari; Zalizah Awang Long; 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.
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.
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.
Co-Authors Abdullah Sajad Aditya Gema Pratama Agus Mardiyono Ahmad Tomi Ali Muhdi Ashary Vermaysha Asmara Andhini Atina, Vihi Bima Setya Gumelar Didi Supriyadi Dwiki Rasya Rahadian Fadilah Nuria Handayani Hasanah, Herliyani Intan Diasih Lindha Juniarta Suseno Lucky Marsella Moh Muhtarom Mohammad Yusuf Nugroho Muhammad Rais Ramadhani Nugroho Arif Sudibyo Nurchim Nurchim Purwanto, Eko Rafel Fernando Rahmawati Desi Tri Wulandari Rico Yoga Pradana Septi Dwi Supriati Sofa Marwati Sri Sumarlinda Sulami, Atik Syaiful Nur Wardani Viona Putri Ardiana Yuliana Dewi Proboningrum Yusmawan Dwi Suseno Zalizah Awang Long Zalizah Awang Long