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All Journal JOIV : International Journal on Informatics Visualization Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Juisi (Jurnal Informatika dan Sistem Informasi) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Information Technology (JIfoTech) Jurnal Abdimas Bina Bangsa DEVOTE: Jurnal Pengabdian Masyarakat Global Jurnal INFOTEL SAINTEK Infact: Jurnal Sains dan Komputer DEVOTE: Jurnal Pengabdian Masyarakat Global
Haeni Budiati
Universitas Kristen Immanuel
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : Progresif: Jurnal Ilmiah Komputer

 1 
Implementasi Metode K-Nearest Neighbor Pada Sentimen Masyarakat Terkait Pelaksanaan KTT G20 Anan Sosmita Lase; Sunneng Sandino Berutu; Haeni Budiati
Progresif: Jurnal Ilmiah Komputer Vol 19, No 2: Agustus 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i2.1236

Abstract

Indonesia is the first Asian country to be appointed to lead the 20th global summit (G20 Summit) and it will be held in Bali, Indonesia. The successful implementation of the G20 Summit attracted public attention, triggering various kinds of public sentiment (opinion) regarding the G20 Summit. With public sentiment, the government can find out the views or opinions of the public regarding the G20 Summit that has been held. Public sentiment can be in the form of positive sentiment or negative sentiment, for this reason a process of classification and analysis of these sentiments is needed. The process of classifying public sentiment starts from the problem identification process, then crawling data to retrieve Tweets from Twitter, data preprocessing to process data, data labeling, KNN-based model training, classification model testing and evaluation. The results of the sentiment analysis show the results of the accuracy of 97.75% accuracy, 100% precision and 97.71% recall.Keywords: Sentiment analysis; K-Nearest Neighbor, G20 summit implementation AbstrakIndonesia merupakan negara Asia pertama yang di tunjuk untuk memimpin Konferensi Tingkat Tinggi Global 20 (KTT G20) dan dilaksanakan di bali, Indonesia. Terlaksananya KTT G20 sukses menarik perhatian publik sehingga memicu munculnya berbagai macam sentimen (opini) masyarakat mengenai KTT G20. Dengan adanya sentimen masyarakat pemerintah dapat mengetahui bagaimana pandangan ataupun opini masyarakat terkait KTT G20 yang telah di laksanakan. Sentimen masyarakat dapat berupa sentimen positif ataupun sentimen negatif, untuk itu di perlukan proses klasifikasi dan analisis terhadap sentimen tersebut. Proses pengklasifikasian sentimen masyarakat di mulai dari proses identifikasi masalah, lalu crawling data untuk mengambil tweet dari Twitter, preprocessing data untuk mengolah data, pelabelan data, training model berbasis KNN, uji model klasifikasi dan evaluasi. Hasil analisis sentimen menunjukkan tingkat akurasi 97,75%, presisi 100% dan recall 97,71%.Kata kunci: Analisis sentimen; K-Nearest Neighbor; Pelaksanaan KTT G20
Komparasi Metode K-NN Dan K-Means Untuk Klasifikasi Buah Mangga Apriani Apriani; A. R. Himamunanto; Haeni Budiati
Progresif: Jurnal Ilmiah Komputer Vol 20, No 2: Agustus 2024 (Naskah ACCEPTED)
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v20i2.2152

Abstract

This Research aim of comparing the modified K-means method for classification processing of training models (Supervised) with the K-NN method using the Mango Arumanis, Golek, Madu and Gedong classes. Feature extraction used in processing classification methods is based on shape characteristics consisting of metric and eccentricity. The research results obtained that the percentage precision of the K-NN method was Arumanis: 0.9%, Madu: 0.9%, Gedong: 0.9%, Golek: 0.8%, and the K-Means method was Arumanis: 0.7%, Honey: 0.9%, Gedong: 0.9%, Golek 0.6%. The recall percentage of the K-NN method is Arumanis: 0.90%, Madu: 0.75%, Gedong: 1.00%, Golek: 0.89% and the K-means method is Arumanis: 0.70%, Madu: 0 .64%, Gedong: 1.00%, Golek: 0.86%. The accuracy percentage of the K-NN classification method is Arumanis: 94.59%, Madu: 89.74%, Gedong: 97.22%, Golek: 92.11% and the K-Means method is Arumanis: 83.78%, Madu: 83.78%, Gedong: 96.88%, Golek: 86.11%. For global precision, recall and accuracy values, the K-NN method is greater than the K-Means method. Thus, the K-Means classification method which was modified to use supervised training data is still not as good as the K-NN method in classifying mango fruit types. It is hoped that the accuracy of the method for classifying mango fruit plant types by extracting shape characteristics can obtain uniform shape quality.Keywords: Image Processing; Feature Extraction; K-means, K-NN; Metric, EccentricityAbstrakPenelitian dengan tujuan komparasi Metode K-means yang dimodifikasi untuk pemrosesan klasifikasi model pelatihan (Supervised) dengan MetodeK-NN mempergunakan kelas Mangga Arumanis, Golek, Madu dan Gedong. Ekstraksi ciri yang dipergunakan dalam pemrosesan metode klasifikasi berdasarkan ciri bentuk yang terdiri dari metric dan eccentricity. Hasil penelitian memperoleh presentase precision metode K-NN adalah Arumanis: 0,9%, Madu: 0,9%, Gedong: 0,9%, Golek: 0,8%, dan metode K-Means adalah Arumanis: 0,7%, Madu: 0,9%, Gedong: 0,9%, Golek 0,6%. Presentase recall metode K-NN adalah Arumanis: 0,90%, Madu: 0,75%, Gedong: 1,00%, Golek: 0,89% dan metode K-means adalah Arumanis: 0,70%, Madu: 0,64%, Gedong: 1,00%, Golek: 0,86%. Presentase Accuracy metode klasifikasi K-NN adalah Arumanis: 94,59%, Madu: 89,74%, Gedong: 97,22%, Golek: 92,11% dan metode K-Means adalah Arumanis: 83,78%, Madu: 83,78%, Gedong: 96,88%, Golek: 86,11%. Untuk nilai precision, recall dan accuracy secara global adalah metode K-NN lebih besar daripada metode K-Means. Dengan demikian, metode klasifikasi K-Means yang dimodifikasi untuk dapat mempergunakan data pelatihan (supervised) masih belum mampu sebaik Metode K-NN dalam klasifikasi jenis buah mangga. Diharapkan akurasi metode klasifikasi jenis tanaman buah mangga dengan ekstraksi ciri bentuk dapat memperoleh kualitas bentuk yang seragam.Kata kunci: Pengolahan Citra, Ekstraksi Ciri, K-means, K-NN, Metric, Eccentricity
Analisis Performa Metode Yolo Untuk Deteksi Hyperlipidemia Berdasarkan Klasifikasi Citra Corneal Arcus Joseph Supriadi; Agustinus Rudatyo Himamunanto; Haeni Budiati
Progresif: Jurnal Ilmiah Komputer Vol 20, No 2: Agustus 2024 (Naskah ACCEPTED)
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v20i2.2088

Abstract

Hyperlipidemia is a medical condition with high blood lipid levels that increase the risk of cardiovascular disease. A physical indicator of hyperlipidemia is Corneal Arcus, a white ring around the cornea. This study analyzes the ability of the YOLO (You Only Look Once) method to detect and classify Corneal Arcus in eye images. The dataset consists of 348 eye images in three categories: normal, at-risk, and Corneal Arcus. Results show the YOLO model achieved 88.9% accuracy in detecting Corneal Arcus, with precision, recall, F1-score, and mean average precision (MAP) of 88.9%, 89.2%, 88.8%, and 88.9%, respectively. These findings indicate significant potential for the YOLO method in technical applications within informatics. Although not yet validated for medical use, this research aims to share basic scientific ideas.Keywords: YOLO; Hyperlipidemia; Corneal Arcus; Image Classification AbstrakHyperlipidemia adalah kondisi medis dengan kadar lipid darah tinggi yang meningkatkan risiko penyakit Kardiovaskular. Indikator fisik hyperlipidemia adalah Corneal Arcus, cincin putih di sekitar kornea. Penelitian ini menganalisis kemampuan metode YOLO (You Only Look Once) dalam mendeteksi dan mengklasifikasikan Corneal Arcus pada citra mata. Dataset terdiri dari 348 gambar mata dalam tiga kategori: normal, berisiko, dan Corneal Arcus. Hasil menunjukkan model YOLO mencapai akurasi 88,9% dalam mendeteksi Corneal Arcus, dengan presisi, recall, F1-score, dan mean average precision (MAP) masing-masing sebesar 88,9%, 89,2%, 88,8%, dan 88,9%. Temuan ini menunjukkan potensi besar metode YOLO dalam aplikasi teknis di bidang informatika. Meskipun belum tervalidasi untuk penggunaan medis, hasil ini bertujuan untuk membagikan ide ilmiah dasar.Kata kunci: YOLO; Hyperlipidemia; Corneal Arcus; Klasifikasi Citra;
Co-Authors A. R. Himamunanto Agus Dirgahayu Zendrato Agustinus Rudatyo Himamunanto Anan Sosmita Lase Antonius Bima Murti Wijaya Apriani Apriani Barita Suci Vernando Zebua Delima Zai Farelia Devi Julia Putri Fornieli Gulo Gogor C Setyawan Gogor Christmass Setyawan Heri Wijayanto Hermas Rizzal Indriyani Hulu Jatmika Jatmika Jatmika Johni Revormasi Ziliwu Joseph Supriadi Juli Supriyanto Gea Kristian Juri Damai Lase Liefson Jacobus, Liefson Lukas Agung Heriputranto Peter1 Pius Hermanto Nehe Sunneng Sandino Berutu Surjawirawan Dwiputranto Timothy Ageng Pracaya Yeong-Chin Chen Yo’el Pieter Sumihar