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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Khatulistiwa Informatika Jurnal Penelitian Pendidikan IPA (JPPIPA) Insect (Informatics and Security) : Jurnal Teknik Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Inotera J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Ilmiah Mandala Education (JIME) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) DECODE: Jurnal Pendidikan Teknologi Informasi Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Journal of Advanced Health Informatics Research
Muntiari, Novita Ranti
Politeknik Bisnis Kaltara
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Pengembangan Sistem Inventory Alat Tulis Kantor (ATK) Berbasis Web Umar, Rusydi; Muntiari, Novita Ranti; E, Ermin; Bustomi, Iqbal; Tella, Fitriyani
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 1 (2020): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v4i1.190

Abstract

Developments in the technological era are increasingly developing and people's needs vary with the desire to accelerate a job in a systematic and more effective and efficient manner. The problems faced by PT. XYZ in supplies of office equipment are still poorly organized and there is no reporting on the use of office stationery (ATK) and inhibitors of employee productivity because there is no precise inventory information and delays in making reports. Facilitates the company in the process of inventory input Application of the method in this research is to use the classic life cycle (CLC) method, known as the process design, which is carried out sequentially, with the research stages beginning with analysis, design, coding and testing.From the several stages that have been carried out in this researcher can produce reports stock of goods and office stationery information updated to users or visitors.
A Comparative Sentiment Analysis of Computer Engineering Student Feedback Using Decision Trees and SVM Hanif, Kharis Hudaiby; Arif Fadllullah; Novita Ranti Muntiari; Irgi Ahmad Fahrezi
Jurnal Inotera Vol. 10 No. 1 (2025): January-June 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss1.2025.ID436

Abstract

The University of Borneo Tarakan, like many Indonesian universities, is committed to continuous quality improvement in education services. A crucial aspect of this improvement is gathering and analyzing student feedback to enhance lecturer performance. This research focuses on analyzing student comments using sentiment analysis, a technique that categorizes text into positive, negative, and neutral sentiments. To achieve this, two machine learning algorithms were employed: Decision Trees and Support Vector Machines (SVM). The research involved two approaches: Lexicon-Based Sentiment Analysis and TF-IDF word weighting. The Lexicon-Based approach compared the automated sentiment classification with manual human categorization to assess accuracy. The TF-IDF method, on the other hand, aimed to improve classification accuracy by assigning weights to words based on their frequency and importance. The experimental results demonstrated that Decision Trees outperformed SVM in terms of classification accuracy, achieving 95.454546% compared to 94.805194%. This finding suggests that Decision Trees is a more effective technique for sentiment analysis of student comments in this specific
A Bibliometric Analysis of Knowledge Distillation in Medical Image Segmentation Muntiari, Novita Ranti; Rania Majdoubi; Rajiansyah
Journal of Advanced Health Informatics Research Vol. 2 No. 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i3.297

Abstract

This study conducts a bibliometric analysis and systematic review to examine research trends in the application of knowledge distillation for medical image segmentation. A total of 806 studies from 343 distinct sources, published between 2019 and 2023, were analyzed using Publish or Perish and VOSviewer, with data retrieved from Scopus and Google Scholar. The findings indicate a rising trend in publications indexed in Scopus, whereas a decline was observed in Google Scholar. Citation analysis revealed that the United States and China emerged as the leading contributors in terms of both publication volume and citation impact. Previous research predominantly focused on optimizing knowledge distillation techniques and their implementation in resource-constrained devices. Keyword analysis demonstrated that medical image segmentation appeared most frequently with 144 occurrences, followed by medical imaging with 110 occurrences. This study highlights emerging research opportunities, particularly in leveraging knowledge distillation for U-Net architectures with large-scale datasets and integrating transformer models to enhance medical image segmentation performance
DETEKSI STANTING PADA BALITA DENGAN MENGGUNAKAN PERBANDINGAN ALGORITMA MACHINE LEARNING Muntiari, Novita Ranti; Hanif, Kharis Hudaiby; Asma, Asma; Herawati, Lily
Jurnal Khatulistiwa Informatika Vol 13, No 1 (2025): Periode Juni 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jki.v13i1.25132

Abstract

Deteksi stanting pada balita adalah proses identifikasi dini untuk mendeteksi gangguan pertumbuhan yang terlihat dari tinggi badan yang berada di bawah standar usia. Stunting merupakan indikator dari masalah gizi kronis yang disebabkan oleh kekurangan asupan gizi dalam jangka waktu yang lama, sering kali diperburuk oleh infeksi berulang dan kondisi sosial ekonomi yang tidak mendukung. Stunting, yang merupakan kegagalan pertumbuhan anak akibat kekurangan gizi jangka panjang, Merupakan masalah kesehatan masyarakat yang rumit dengan dampak yang berlangsung dalam jangka panjang. Dalam penelitian ini, dibangun sistem yang menggunakan algoritma machine learning, yaitu decision tree, SVM, KNN, random forest, naïve bayes,  logistic regression, untuk mengukur tingkat akurasi masing-masing algoritma. Pengujian dilakukan dengan menggunakan 30% data untuk pengujian dan 70% untuk pelatihan. Hasil pengujian menunjukkan tingkat akurasi algoritma sebagai berikut: decision tree mencapai akurasi 99%, SVM 95%, logistic regression 74%. naïve bayes 48%, KNN 99%, dan  random forest 99%. Oleh karena itu, algoritma decision tree, KNN, dan random forest menghasilkan akurasi tertinggi, yaitu 99% yang menunjukkan bahwa algoritma ini lebih efektif untuk mendeteksi stanting pada balita.
PENERAPAN ALGORITMA DECISION TREE, SVM, NAÏVE BAYES DALAM DETEKSI STUNTING PADA BALITA Hanif, Kharis Hudaiby; Muntiari, Novita Ranti
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 1 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No1.pp105-109

Abstract

Stunting is a toddler's body condition that is short according to body length according to age (PB/U), ≤ 2 Standard Deviations (SD), with a z-score between -3 standard deviations (SD). Where checking the stunting status of toddlers takes quite a long time because it is done manually and is also prone to errors. Therefore, it is hoped that a system can classify toddler examination data quickly and accurately to predict children's stunting status. Building a system that uses an algorithm to classify the stunting status of toddlers usingdecision tree, naïve bayes, andSVM. With what level of accuracy is the best of the 3 algorithms? Results from testing with 30% testing data and 70% training data using an algorithmdecision tree, naïve bayes, and SVM. Accuracy level test resultsdecision tree by 99%,naïve bayes of 48%, and SVM of 95%. So, the algorithm with the highest level of accuracy isdecision tree amounts to 99%. Wallet hiredecision tree better for detecting stunting in toddlers
Klasifikasi Penyakit Kanker Payudara Menggunakan Perbandingan Algoritma Machine Learning Muntiari, Novita Ranti; Hanif, Kharis Hudaiby
Jurnal IT UHB Vol 3 No 1 (2022): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v3i1.766

Abstract

Salah satu penyakit yang sangat ditakuti adalah kanker payudara, kanker payudara termasuk penyakit yang mematikan pada Wanita. Kanker payudara dikelompokkan menjadi dua jenis, yaitu ganas dan jinak. Dalam pengelompokkan jenis ini dibutuhkan metode yang cepat agar dapat membantu dalam pengambilan keputusan. Machine learning adalah bagian dari bidang kecerdasan buatan yang berfokus pada penerapan algoritma dan metode khusus untuk prediksi, pengenalan pola, dan klasifikasi. Sehingga Machine learning dapat membantu dalam mengelompokkan jenis kanker payudara. Penelitian ini menggunakan 7 algoritma yaitu neural network, decision tree, naïve bayes, k-nearest neighbor, logistic regresion, random forest, dan support vector machines dalam mengelompokkan jenis kanker payudara. Dengan pengolahan data menggunakan aplikasi RapidMiner didapat bahwa nilai akurasi dari algoritma logistic regresion, decision tree, naïve bayes dan k-nearest neighbor memiliki nilai akurasi yang sama tinggi yaitu sebesar 95,00%. Sehingga algoritma logistic regresion, decision tree, naïve bayes dan k-nearest neighbor mempercepat pengambilan keputusan dalam memprediksi dalam klasifikasi penentuan jenis kanker payudara.
Perbandingan Algoritma Regresi Logistik, Support Vector Machine, dan Gradient Boosting Pada Analisis Sentimen Data Komentar Siswa Muntiari, Novita Ranti; Kharis Hudaiby Hanif; Indah Chairun Nisa
Jurnal IT UHB Vol 4 No 2 (2023): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v4i2.1286

Abstract

Evaluasi pengajaran dilakukan menggunakan aplikasi Digital Teacher Assessment (DITA) belum melibatkan klasifikasi pengelompokkan. Data komentar yang terkumpul dikelompokkan menjadi tiga kategori yaitu komentar positif, negatif, dan netral. Berdasarkan kategori komentar membutuhkan analisis sentimen dalam mengelompokkan komentar tersebut. Analisis sentimen menggunakan lexicon based. Selanjutnya data komentar tersebut diberi bobot menggunakan TF-IDF sebelum diklasifikasikan dan dievaluasi. Dalam penelitian ini menggunakan algoritma regresi logistik, support vector machine (SVM), dan gradient boosting. Hasil penelitian menunjukkan perbandingan akurasi dari algoritma regresi logistik, support vector machine (SVM), dan gradient boosting dengan algoritma gradient boosting memiliki tingkat akurasi yang paling tinggi yaitu 97,5%. Dari hasil penelitian dapat disimpulkan bahwa algoritma gradient boosting memiliki tingkat akurasi lebih baik dalam mengklasifikasi data analisis sentimen komentar siswa.
Pengabdian Sebagai Dewan Juri Lomba Kompetensi Siswa (LKS) Bidang Web Technologies SMK Tingkat Provinsi Kalimantan Utara Muntiari, Novita Ranti; Haryansyah
Jurnal Pengabdian Masyarakat - PIMAS Vol. 3 No. 2 (2024): Mei
Publisher : LPPM Universitas Harapan Bangsa Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/pimas.v3i2.1422

Abstract

The Student Competency Competition is an annual competition between students at vocational school level according to the areas of expertise taught at the participating vocational schools. This worksheet is equivalent to the OSN (National Science Olympiad) which is held in middle/high school. This activity is one part of a series of selections to get the best students from all over Indonesia who will be further guided by their respective competition field teams and will be included in international level skill competencies. The aim of the community service activities carried out through this LKS competition is for the Court Team to contribute as a jury in the competition Web Technologies. The role of the jury is as the center for determining the provincial level LKS competition, so that the results of the selection of the best students will represent North Kalimantan Province to enter the competition at the national level. The final result of this community service activity is a decision from the council regarding the competition assessment process in determining the winner of the LKS competition at the North Kalimantan Province level.
Pengaruh Perbandingan Video E-Learning dalam meningkatkan Kognitif Psikomotor terhadap Asuhan Keperawatan Jiwa pada Klien Resiko Perilaku Kekerasan Bagi Mahasiswa Keperawatan Sriyanti, Nur; Muntiari, Novita Ranti; Rejin, Rosaleni
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.13001

Abstract

This study aimed to analyze the comparative effectiveness of e-learning videos created by course lecturers versus externally produced videos in enhancing nursing students' cognitive and psychomotor skills regarding psychiatric nursing care for clients at risk of violent behavior. A quasi-experimental design with a pretest-posttest approach was employed. Student cohorts were divided into an intervention group, which used lecturer-made videos, and a control group, which used externally made videos. The findings revealed a substantial improvement in the intervention group, with cognitive skills increasing by 86.5% and psychomotor skills by 69.77%. In contrast, the control group showed no significant increase. This indicates that lecturer-created videos, likely due to their tailored relevance to the specific curriculum and learning context, are a more potent educational tool. It is concluded that e-learning videos developed by the course lecturers are significantly more effective than external videos in improving both the knowledge and practical skills of nursing students. The use of curated, internally developed video media is therefore highly recommended for optimizing learning outcomes in specialized nursing education.  
Co-Authors Abdul Fadlil Ana Sriekaningsih Andri Yogi Adyatma Prasetyo Arif Fadllullah Asma Asma Bustomi, Iqbal E, Ermin Ermin Ermin Hanif, Kharis Hudaiby Haryansyah Herawati, Lily Indah Chairun Nisa Irgi Ahmad Fahrezi Khoirun Nisa Muhammad Yusril Purwono, Purwono Putri Ananda Ramadhani Rajiansyah Rania Majdoubi Rejin, Rosaleni Rusydi Umar Sriyanti, Nur Sunardi, Sunardi Tella, Fitriyani