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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Pendidikan Teknologi dan Kejuruan Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Eksplora Informatika IJEBD (International Journal Of Entrepreneurship And Business Development) JURIKOM (Jurnal Riset Komputer) Jurnal Teknologi Informasi dan Multimedia Jurnal Praksis dan Dedikasi Sosial Indonesian Journal of Electrical Engineering and Computer Science Jurnal Manajemen dan Teknologi Informasi Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Ni Made Ika Marini Mandenni
Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Social Sciences Other

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Analisis Sentimen Publik Terkait Kekerasan Seksual di Indonesia dengan Algoritma Naïve Bayes dan SVM Nalista, Ni Made Naila; Mandenni, Ni Made Ika Marini; Suarjaya, I Made Agus Dwi
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8556

Abstract

Semakin meningkatnya kasus kekerasan seksual yang terjadi di Indonesia, dan media sosial merupakan ruang bagi masyarakat Indonesia untuk mengekspresikan pendapat. The increasing number of sexual violence cases in Indonesia, along with the role of social media as a space for the public to express their opinions, forms the basis for this research. The study aims to classify various types of public sentiment expressed on X (formerly Twitter) and Instagram comments by applying two algorithms for comparison: Naïve Bayes and SVM. Several processes carried out, including data collection from social media, data preprocessing, manual labeling, and the implementation of both algorithms on the processed dataset. The data sources utilized are posts written in Indonesian on X (Twitter) and Instagram, focusing on issues of sexual violence in Indonesia. The sentiment analysis results were grouped into three main categories: positive, negative, and neutral. The outcomes show that SVM achieved an accuracy of 82.17% using an 80:20 data split without applying GridSearch for optimization. The SVM results outperformed those of Naïve Bayes, which achieved an accuracy of 78.92%. This investigation leads to the conclusion that SVM is more optimal in analyzing public sentiment related to sexual violence in Indonesia compared to Naïve Bayes. The sentiment analysis results from social media regarding sexual violence in Indonesia show that the majority of sentiments are neutral, with the dataset being dominated by informative content, case reports without emotional expression, and off-topic comments
AUDIT TATA KELOLA TEKNOLOGI INFORMASI DI RSUD X MENGGUNAKAN FRAMEWORK COBIT 2019 I Komang Satria Wibawa; Anak Agung Ngurah Hary Susila; Ni Made Ika Marini Mandenni
Jurnal Manajemen dan Teknologi Informasi Vol. 15 No. 2 (2025): Jurnal Manajemen dan Teknologi Informasi
Publisher : Fakultas Teknik dan Informatika Universitas PGRI Mahadewa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59819/jmti.v15i2.5425

Abstract

An IT governance audit at X Hospital was conducted due to various issues affecting service quality and system performance. Critical points included an overloaded database that resulted in suboptimal website information updates, inadequate server specifications to run the website when users were active, poorly managed network cables that made the server vulnerable to disruptions, and an under-protected IT unit in website management. The study aimed to identify critical points, elicit IT process capabilities, and provide improvement recommendations based on the COBIT 2019 Framework. The methods used included interviews, observations, business goal mapping and alignment, and a capability level questionnaire. The audit results identified five main domains, namely APO12, BAI10, DSS02, DSS03, and DSS04, all at capability level 2. Recommendations included IT risk integration, asset management, accelerated incident handling, improved service and satisfaction, and rapid adaptation to increase capabilities to levels 3, 4, and 5.
Analisis Sentimen Publik Terkait Kekerasan Seksual di Indonesia dengan Algoritma Naïve Bayes dan SVM Nalista, Ni Made Naila; Mandenni, Ni Made Ika Marini; Suarjaya, I Made Agus Dwi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8556

Abstract

Semakin meningkatnya kasus kekerasan seksual yang terjadi di Indonesia, dan media sosial merupakan ruang bagi masyarakat Indonesia untuk mengekspresikan pendapat. The increasing number of sexual violence cases in Indonesia, along with the role of social media as a space for the public to express their opinions, forms the basis for this research. The study aims to classify various types of public sentiment expressed on X (formerly Twitter) and Instagram comments by applying two algorithms for comparison: Naïve Bayes and SVM. Several processes carried out, including data collection from social media, data preprocessing, manual labeling, and the implementation of both algorithms on the processed dataset. The data sources utilized are posts written in Indonesian on X (Twitter) and Instagram, focusing on issues of sexual violence in Indonesia. The sentiment analysis results were grouped into three main categories: positive, negative, and neutral. The outcomes show that SVM achieved an accuracy of 82.17% using an 80:20 data split without applying GridSearch for optimization. The SVM results outperformed those of Naïve Bayes, which achieved an accuracy of 78.92%. This investigation leads to the conclusion that SVM is more optimal in analyzing public sentiment related to sexual violence in Indonesia compared to Naïve Bayes. The sentiment analysis results from social media regarding sexual violence in Indonesia show that the majority of sentiments are neutral, with the dataset being dominated by informative content, case reports without emotional expression, and off-topic comments
IMPLEMENTASI MACHINE LEARNING UNTUK MENINGKATKAN KUALITAS OPERASIONAL SERVICE KENDARAAN DENGAN METODE RANDOM FOREST DAN LOGISTIC REGRESSION Mandenni, Ni Made Ika Marini; Wiratama, I Putu Bayu Adhya; Setiawan, Ariyono; Putri, Gusti Agung Ayu; Dana, I Putu Ngurah Krisna
Jurnal Praksis dan Dedikasi Sosial Vol. 7 No. 2 (2024)
Publisher : Universitas Negeri Malang

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

Abstract

IMPLEMENTATION OF MACHINE LEARNING TO IMPROVE THE QUALITY OF VEHICLE SERVICE OPERATIONS WITH RANDOM FOREST AND LOGISTIC REGRESSION METHODSVehicle motor repairs (service) are an important aspect for motor vehicle owners to undertake. This activity is carried out by automotive workshops to ensure that the customer's vehicle is in prime condition. To boost sales, some automotive workshops offer various promotional packages to attract customer interest. However, in practice, this is done manually by workshop staff, resulting in suboptimal performance in offer presentations (customer calls). This research aims to build a recommendation system for package deals and offer dates to enhance the quality of customer calls in the operations of automotive workshops using Random Forest and Logistic Regression. The dataset used is operational data from customer calls at one automotive workshop in Bali. The Random Forest model achieves 91 percent accuracy, while Logistic Regression achieves 72 percent accuracy. The system developed can be used to recommend good package deals and offer dates to customers.Perbaikan kendaraan bermotor (service) merupakan hal penting untuk dilakukan bagi pemilik kendaraan bermotor. Kegiatan ini dilakukan oleh bengkel otomotif untuk memastikan kondisi kendaraan customer dalam kondisi prima. Untuk meningkatkan penjualan, beberapa bengkel otomotif menawarkan berbagai paket promo untuk menarik minat customer. Namun dalam pelaksanaannya, hal ini dilakukan secara manual oleh staff bengkel yang mengakibatkan performa penawaran (customer call) kurang optimal. Penelitian ini bertujuan untuk membangun sistem rekomendasi paket dan tanggal penawaran untuk meningkatkan kualitas customer call pada operasional bengkel otomotif menggunakan Random Forest dan Logistic Regression. Dataset yang digunakan adalah data operasional customer call salah satu bengkel otomotif di Bali. Model Random Forest mencapai akurasi 91 persen dan Logistic Regression mencapai akurasi 72 persen. Sistem yang dibangun dapat digunakan untuk merekomendasikan paket dan tanggal penawaran yang baik untuk ditawarkan kepada customer.
PERAMALAN ZONA POTENSI PENANGKAPAN IKAN DI PERAIRAN PULAI BALI DENGAN METODE FORECASTING LSTM I Putu Agung Bayupati; I Putu Dedy Ardana Putra; Ni Made Ika Marini Mandenni
Jurnal Pendidikan Teknologi dan Kejuruan Vol. 23 No. 1 (2026): Edisi Januari 2026
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jptk-undiksha.v23i1.101007

Abstract

Data klorofil-a dan suhu permukaan laut di wilayah perairan sekitar Pulau Bali dapat digunakan untuk meramalkan zona potensi penangkapan ikan dengan metode Long Short-Term Memory (LSTM) dan memprediksi zona potensi penangkapan ikan dengan metode overlay citra dengan aplikasi GIS. Karakteristik zona potensi penangkapan ikan yang digunakan adalah ikan pelagis dengan karakter klorofil-a 0,2-2 mg/m3 dan suhu permukaan laut 25-32 oC, data diperoleh dari web Copernicus EU. TensorFlow diatur statis untuk memperoleh pelatihan yang bersifat reproducible. Model LSTM yang digunakan adalah stacked LSTM dengan dua LSTM layer, dropout, dan dense layer. Model dievaluasi dengan strategi evaluasi train-test split dan metrik root mean square error. Peramalan dilakukan dengan dua cara yaitu satu model untuk semua titik koordinat, dan model berbeda untuk setiap titik koordinat. Hasil peramalan dengan kedua cara mendapatkan hasil dengan perbedaan nilai klorofil-a dan SPL yang cukup kecil. Hasil prediksi zona potensi penangkapan menghasilkan peta dengan tiga wilayah berpotensi.
Co-Authors A.A. Istri Alit Dwi Purnamaningrat Ajeng Wahyuningtyas Anak Agung Ketut Agung Cahyawan Wiranatha Anak Agung Kompiang Oka Sudana Anak Agung Ngurah Hary Susila Anak Agung Putu Mahendra Putra Assegaf, Achmad Jefri Ayu Widyastuti Purnamasari Ayu Wirdiani Clarissa Anindita Damayanthi, Ni Luh Putu Ari Yunia Dana, I Gusti Ngurah Krisna Dwi Putra Githa Dwi Rusjayanthi, Dwi Erna Yulianti Fatmawati Fatmawati Gede Yudha Prema Pangestu Gideon Setya Budiwitjaksono Gilang Ramadhan, Rizki Aditiya Gusti Agung Ayu Putri Hafidz Amarul M I Dewa Gede Wahya Dhiyatmika I Gede Susrama Mas Diyasa I Gede Susrama Masdiyasa I Gusti Made Ngurah Ardi Yasa I Kadek Erik Priyanto I Kadek Owen Nirvana Kaskora I Ketut Adi Purnawan I ketut Gede Darma Putra I Komang Agus Ady Aryanto I Komang Satria Wibawa I Made Agus Dwi Suarjaya I Made Dharmawan Setiadi I Made Oka Mahendra Putra I Made Sukarsa I Nyoman Piarsa I Putu Agung Bayupati I Putu Agus Eka Pratama I Putu Dedy Ardana Putra Ida Ayu Wahyu Kumara Putri Ida Bagus Gede Jayeng Gotama Ilham Ade Widya Sampurno Irianto Yohanes Sampe James Kawilarang Mohammad Rafka Mahendra A Muhammad Alam Pasirulloh Nalista, Ni Made Naila Narayana, I Putu Kevin Ari Ni Putu Intan Waindika Dharma NYOMAN DITA PAHANG PUTRA, NYOMAN Pande Made Satrya Dinata Panji Wiratama Santoso Parayana, I Putu Mega Putra Prasta, I Gede Andi Primanggara Gamaswara Putra Githa, Dwi Putra, I Putu Bayu Aditya Putu Anantha Prasetya Yogantara Putu Arismawan Jaya Kusuma Putu Arya Hening Tryasthana Putu Eka Suryadana Putu Riyaldi Putra Narendra Putu Wira Buana Rangga Laksana A Redana, Made Gede Gumiar Putra Santoso, Panji Wiratama Sasmita, Gusti Made Arya Setiawan, Ariyono setiawan, kadek dede Vira Deva, Made Marshall Widiana Putra Winarta Wiratama, I Putu Bayu Adhya Yusliza Binti Mohd Yasin