Article Per Year (5 Year)
p-Index From 2021 - 2026
4.145
P-Index
This Author published in this journals
All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Matematika Jurnal Ilmu Dasar CAUCHY: Jurnal Matematika Murni dan Aplikasi JSI: Jurnal Sistem Informasi (E-Journal) Kubik POSITIF Jurnal Sains Matematika dan Statistika Jurnal Penelitian Sains JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JIEET (Journal of Information Engineering and Educational Technology) Jurnal Pengabdian Pada Masyarakat BAREKENG: Jurnal Ilmu Matematika dan Terapan Teorema: Teori dan Riset Matematika Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Jurnal Teknologi Terapan Jurnal Matematika UNAND INTEGRITAS : Jurnal Pengabdian Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Diseminasi : Jurnal Pengabdian kepada Masyarakat KUBIK: Jurnal Publikasi Ilmiah Matematika Jurnal Teknologi Bulletin of Applied Mathematics and Mathematics Education Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Pelita Sriwijaya Limits: Journal of Mathematics and Its Applications AbdiMuh: Jurnal Pengabdian Masyarakat
Novi Rustiana Dewi
Jurusan Matematika Fakultas MIPA Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 34 Documents
Search

 1   2   3   4 
Algoritma Support Vector Machine (SVM) dan Adaptive Boosting (AdaBoost) untuk Klasifikasi Penyakit Kanker Paru-paru Pasma Azzahra; Ravisha Keyna Anduwi; Anita Desiani; Novi Rustiana Dewi; Indri Ramayanti
JSI: Jurnal Sistem Informasi (E-Journal) Vol 17 No 2 (2025): JSI: Jurnal Sistem Informasi (E-Journal)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v17i2.319

Abstract

Kanker paru-paru adalah jenis kanker yang tumbuh dalam organ paru-paru di mana perubahan sel paru-paru yang tidak normal terjadi. Penyakit ini disebabkan oleh beberapa kebiasaan seperti merokok, alergi, polusi udara, dan sebagainya. Kanker paru-paru termasuk jenis kanker yang mematikan. Deteksi dini dapat dilakukan dengan pendekatan matematis yaitu data mining. Penelitian ini bertujuan untuk menganalisis perbandingan kinerja klasifikasi antara algoritma Support Vector Machine (SVM) dan Adaptive Boosting (AdaBoost). Analisis komparatif ini dilakukan untuk mengidentifikasi algoritma mana yang menunjukkan performa paling optimal dalam penanganan data kanker paru-paru. Teknik uji yang dilakukan pada penelitian ini adalah percentage split dan K-Fold cross validation. Hasil pengujian dengan metode percentage split menunjukkan bahwa algoritma SVM mencapai akurasi 85%, sedangkan algoritma AdaBoost memperoleh akurasi 95%. Sementara itu, pengujian dilakukan menggunakan teknik K-Fold cross validation, akurasi untuk algoritma SVM adalah 88% dan untuk algoritma AdaBoost sebesar 93%. Dapat disimpulkan bahwa metode percentage split dengan algoritma AdaBoost memiliki performa tertinggi dibandingkan metode dan teknik pengujian lainnya, yaitu sebesar 98% sehingga algoritma Adaboost lebih akurat untuk deteksi dini kanker paru-paru. . Kontribusi penelitian ini terletak pada pengembangan sistem pendukung keputusan untuk diagnosis awal kanker paru-paru, yang berpotensi mempermudah tenaga medis dalam tahap deteksi dini.
Improve of Multiobjective Model on the Classification Problem of Food Consumption Levels in Indonesia Eka Susanti; Novi Rustiana Dewi; Arsi Arsi
KUBIK Vol 10 No 1 (2025): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Department of Mathematics, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v10i1.40632

Abstract

Classification is the process of grouping objects based on similarities and differences. In this article, a multi-objective classification model is developed with three objective functions, namely the function that maximizes the values of accuracy, sensitivity and specificity. The developed model is applied to the problem of classifying meat, egg and fish consumption levels. The classification method used is K-Nearest Neighbor (KNN) with three objective functions and the addition of the GridSearchCV module to the KNN calculation. Completion of the multiobjective model using the weighting method and Particle Swam Optimization (PSO). Based on the data, with objective function weights of 1, 2 and 3 respectively being 0.7, 0.15 and 0.15, the results obtained for Rural Areas Meat, Fish and Egg Attributes of the model performance are in good criteria. for Urban Areas Attributes of Meat, Fish and Eggs the model's performance in the criteria is very good. Addition of the GridsearchCV module can facilitate the calculation of the KNN method classification because the model will provide the best k value without having to do repeated calculations.
- Heterogeneous Fleet Vehicle Routing Problem dan Penyelesaiannya Menggunakan Metode Nearest Neighbor : Kasus Pendistribusian Ayam Potong Evi Yuliza; Novi Rustiana Dewi; Sisca Octarina; Indrawati Indrawati
Limits: Journal of Mathematics and Its Applications Vol. 23 No. 2 (2026): Limits: Journal of Mathematics and Its Applications Volume 23 Nomor 2 Edisi Ju
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v23i2.8956

Abstract

Distribution of goods using a heterogeneous fleet of vehicles is an important aspect in a logistics system because it affects distribution efficiency. This study aims to determine the optimal distribution route for broiler chickens using the Heterogeneous Fleet Vehicle Routing Problem (HFVRP) model to minimize the total vehicle mileage. The data used include customer locations, number of requests, vehicle capacity, heterogeneous vehicle types, and distances between locations. The problem is modeled in HFVRP by considering vehicle capacity, each customer is visited exactly once, and vehicles depart and return to the depot. The solution is carried out using the Nearest Neighbor method to generate distribution routes and compared with the optimal solution from the HFVRP model. The results show a total mileage of 88.6 km for the Nearest Neighbor method, while the optimal solution from the HFVRP model produces a total mileage of 121.2 km. Based on these results, the Nearest Neighbor method provides a more efficient route solution in the case of broiler chicken distribution.
Perbandingan Kinerja Algoritma Random Forest dan Gradient Boosting dalam Klasifikasi Risiko Stunting pada Balita Diah Suci Ramadhani; Marisa -; Anita Desiani; Novi Rustiana Dewi; Bambang Suprihatin; Endro Setyo Cahyono; Lucky Indra Kesuma
Jurnal Teknologi Vol 26, No 2 (2026): Agustus 2026
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v26i2.9285

Abstract

Stunting in toddlers is a chronic nutritional problem that can impede physical growth and cognitive development; therefore, early detection is crucial to prevent long-term impacts. This study compared Random Forest and Gradient Boosting algorithms for classifying stunting risks in toddlers. The comparison of both algorithms was conducted to determine the model with better performance in classifying stunting status into four categories: severely stunted, stunted, normal, and tall. Testing was performed using the Percentage Split method (80% training data and 20% testing data) and 10-Fold Cross Validation. Model performance was measured based on accuracy, precision, and recall metrics. The results showed that the Random Forest algorithm produced better and more consistent performance compared to Gradient Boosting across both testing methods. In the Percentage Split method, Random Forest achieved an accuracy of 95.43%, meaning the model was able to accurately predict stunting status in a single test data split, whereas Gradient Boosting only achieved 85.68%, indicating a higher prediction error rate. In the 10-Fold Cross Validation method, Random Forest maintained an accuracy of 94.89%, meaning the model remained consistent despite being repeatedly tested using varied data subsets, while Gradient Boosting decreased to 77.47%, indicating that the model was unstable and sensitive to data variations. Additionally, Random Forest demonstrated stable precision and recall values above 90% across all stunting categories, particularly for the normal and tall categories. In conclusion, the Random Forest algorithm is more effective in classifying stunting risks in toddlers than the Gradient Boosting algorithm.
Co-Authors ABDUL AZIZ ARROHMAN Agustina Bidarti Alfensi Faruk Ali Amran Anita Desiani anita, anita desiani Arhami, Muhammad Arsi Arsi Arsi, Arsi Bambang Suprihatin Bambang Suprihatin BONGOT R. PURBA Des Alwine Zayanti DES ALWINE ZAYANTI Des Alwine Zayanti, Des Alwine Dewi Lestari Dwi Putri Diah Suci Ramadhani Dian Cahyawati DIAN LESTARI Eddy Roflin Eka Susanti Eka susanti Eka Susanti ELI WIDIYANTI Endang Sri Kresnawati ENDANG SRI KRESNAWATI Endro Setyo Cahyono Endro Setyo Cahyono, Endro Setyo Evi Yuliza, Evi Fitri Maya Puspita Fitri Salamah Fitri Yani Herlina Hanum Herlina Hanum, Herlina Husnul Khotimah Indira, Ria Indrawati Indrawati Indrawati Indri Ramayanti Irsyadi Yani Lailatul Karomah Lucky Indra Kesuma Marisa - Muhammad 'Aqil Muhammad Awaludin Djohar Mukhlisah, Nur Muslim Ansori NEYSA ADIRATNA AGMI MASYITHAH Ning Eliyati Nyimas Syarifah Khodijah Oki Dwipurwani Oktavianus Hasiholan Marbun Pasma Azzahra Permata, Anggun Pipin Palinda Prahesti, Wanodya Eka Rahmadita, Suristhia Ramadhan, Ahmad Farhan Rani Elekta Togatorop Ravisha Keyna Anduwi Resnawati Rika Novita Rika Novita Rindarto, Ahmad Robinson Sitepu Safira, Indri Yune serip, Amanda Aulia Rachman SINTA ELPATRIKA ABELIA Sisca Octarina SITI NATASYA MUNAWAROH Sitorus, Dina Suzzete Sugandi Yahdin Supadi, Siti Suzlin Tiara Bella Octalia Yanita, Nurul Fadhila Yosephus D. Sumanto Yuli Andriani Yulia Resti