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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi JIKO (Jurnal Informatika dan Komputer) ILKOM Jurnal Ilmiah Journal of Information Technology and Computer Engineering Patria Artha Technological Journal Edumaspul: Jurnal Pendidikan Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali JURNAL IT: Media Infromasi STMIK Handayani Makassar Jambura Journal of Electrical and Electronics Engineering Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Jurnal JEETech Fokus Elektroda: Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Information System Journal (INFOS) Bulletin of Information Technology (BIT) Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Penelitian Ilmu Pendidikan Indonesia Nobel Community Services Journal
Hazriani, Hazriani
Universitas Handayani Makassar
Humanities 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Clustering Data Penerimaan Mahasiswa Baru Universitas Handayani Makassar Menggunakan Algoritma K-Means A. Ade Rosali Saputra; Samsart Deandi Palumery; Hazriani, Hazriani; Yuyun, Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This research aims to group new student data for the 2022 academic year at Handayani University Makassar by utilizing a data mining process using the K-Means Clustering algorithm. Implementation using Rapidminer software is used to help determine accurate values. The data used in carrying out this research consists of 4 (four) attributes, namely, school origin, average UAS (final semester exam) score, gender and chosen study program. The research process begins by selecting data and then transforming the data into a numerical group. It is hoped that the results of this research can help universities in improving appropriate promotional strategies in each study program at Handayani University, Makassar.
Analisis Sentimen Ulasan Produk di E-Commerce Bukalapak Menggunakan Natural Language Processing Elsa Sera; Hazriani, Hazriani; Mirfan, Mirfan; Yuyun, Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This research discusses the analysis of sentiment in product reviews on E-Commerce Bukalapak using Natural Language Processing (NLP). The study aims to fill the knowledge gap regarding the analysis of product reviews in online stores in Indonesia, specifically Bukalapak. The data used in this research were collected from various product categories, such as clothing, electronics, cosmetics, and others. The method employed in this study was the TF-IDF method to train the Naive Bayes model. The results of the research show that the Naive Bayes model trained using the TF-IDF method achieved an accuracy of 88%. This indicates that the model has good capability in predicting the sentiment of product reviews. The analysis of positive reviews reveals customer satisfaction with product quality, fast delivery, reasonable pricing, and receiving items as expected. On the other hand, the analysis of negative reviews uncovers the mismatch between customer expectations and the actual conditions regarding color, delivery, and product orders. This study contributes to a deeper understanding of sentiment analysis in product reviews on E-Commerce Bukalapak. The insights from this analysis can be utilized by Bukalapak to enhance the quality of their products and services, providing a more satisfying experience for customers.
Penerapan Metode K-Means Clustering Dalam Mengelompokkan Data Penjualan Obat pada Apotek M23 Nurul Azmi; Hafsah HS; Yuyun, Yuyun; Hazriani, Hazriani
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Planning for the need for the right medicines can make the procurement of medicines efficient and effective so that the medicines can be sufficiently available as needed and can be obtained when needed. At the current M23 Pharmacy, sometimes there is a shortage or overstock of medicines. To overcome these problems a data mining method is applied by analyzing drug use to produce information that can be used as drug inventory control and planning. The method used in this study is the K-Means method. The K-Means clustering method aims to group data that has the same characteristics into the same cluster and data that has different characteristics are grouped into other clusters. As for determining the best number of clusters using the Davies Bouldin Index (DBI) method. The results of this study determined that the best number of clusters was 2 clusters, the drug data grouping consisted of cluster 1 with low drug use consisting of 144 types of drugs and cluster 2 with high drug use consisting of 6 types of drugs.
Uji Kinerja K-Means Clustering Menggunakan Davies-Bouldin Index Pada Pengelompokan Data Prestasi Siswa Imam T. Umagapi; Basirung Umaternate; Hazriani, Hazriani; Yuyun, Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This research investigates how the values of clustered datasets, both normalized and non-normalized, influence the computation of Euclidean distance in the K-means algorithm. Additionally, it examines the impact of varying cluster quantities, identified through the elbow method, on the evaluation of the Davies-Bouldin Index (DBI). A dataset comprising 174 records undergoes mining using the CRISP-DM (Cross-Industry Standard Process for Data Mining) approach. In the data preparation phase, the min-max algorithm is applied to ensure that attribute values within the dataset are not diminished relative to each other. Concerning the selection of an optimal K value, the elbow method is employed. In this investigation, two K values exhibit significant mean reduction: the fourth and third cluster quantities. The DBI results for 3 clusters show a smaller value of 0.9250 compared to the DBI result for 4 clusters, which is 1.1584. The fundamental principle of evaluating the Davies-Bouldin Index is that a smaller DBI value (approaching zero but not reaching the minimum) indicates a better cluster. These findings contribute to a better understanding of the evaluation techniques involving the elbow method and Davies-Bouldin Index in clustering analysis and offer insights into the relationship between determining cluster quantities and clustering performance.
Penerapan Algoritma C4.5 dalam Mengidentifikasi Karakteristik Pasien Beresiko Diabetes Nuradha, Nuradha; Andi riski ramadani; Hazriani, Hazriani; Yuyun, Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Diabetes Mellitus is a disease characterized by an increase in glucose as well as an abnormal rise in blood sugar concentration due to insulin deficiency. The International Diabetes Federation (IDF) reports that in 2021, approximately 540 million people worldwide were affected by diabetes, and this number is expected to increase further if the general public's lack of awareness about symptoms that can trigger the diabetes disease continues. This research aims to implement the C4.5 algorithm in predicting diabetes mellitus based on acquired data. The amount of data used is 300 records, where 90% of the data serves as training data and the remaining 10% is test data. The data consists of 6 attributes: age, gender, hypertension, glucose, heart disease, and BMI (Body Mass Index). Based on Gain calculations, the Glucose attribute becomes the root of the decision tree. The tested data in this study achieved an accuracy rate of 77%, precision of 82%, and recall of 64%.
Deteksi Kalori Makanan Tradisional Indonesia Menggunakan Metode Single Shot Multibox Detector (SSD): Calorie Detection of Traditional Indonesian Food Using the Single Shot Multibox Detector (SSD) Method Riswanto, Riswanto; Ahmad, Andani; Hazriani, Hazriani; Tribuana, Dhimas
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1332

Abstract

Tujuan penelitian untuk mengembangkan sistem pendeteksi kalori makanan dengan menggunakan metode Single Shot Multibox Detector (SSD). Juga, bertujuan untuk mengatasi masalah manusia yang kesulitan dalam mengestimasi jumlah kalori yang dikonsumsi dari makanan. Dengan menggunakan model kecerdasan buatan dan bantuan kamera pada perangkat ponsel, pada penelitian ini memungkinkan pengguna untuk melakukan estimasi kalori yang lebih akurat. Sistem ini dirancang secara otomatis untuk mengidentifikasi dan memperkirakan jumlah kalori dalam makanan berdasarkan citra visual. Pemilihan metode SSD didasarkan pada keunggulannya dalam mendeteksi objek dengan tingkat akurasi yang tinggi dan kecepatan pengolahan yang cepat. Proses penelitian melibatkan beberapa tahap, termasuk pengumpulan dataset citra makanan, pelatihan model SSD dengan konfigurasi Hyperparameter pada 40.000 langkah, menggunakan data training sebanyak 90%, validasi 10%, dan testing 10%, serta menggunakan batch size 16 dan learning rate 0.007943453. Hasil eksperimen menunjukkan total loss sebesar 0.1670681 dan mean average precision (mAP) sebesar 65.09%. Jenis makanan berhasil dideteksi dengan baik, dan aplikasi mobile terkait mampu mengestimasi kalori makanan setelah deteksi jenis makanan. Meskipun demikian, penelitian mengidentifikasi beberapa tantangan, terutama dalam meningkatkan akurasi deteksi pada makanan dengan struktur kompleks atau variasi presentasi yang ekstensif. Temuan dari penelitian ini diharapkan dapat memberikan kontribusi penting dalam pengembangan sistem pendukung keputusan untuk pemantauan otomatis asupan kalori. 
DETEKSI MALFORMASI UTERUS MELALUI CITRA HISTEROSALPINGOGRAFI MENGGUNAKAN DEEP LEARNING Baital, Muhammad Syarif; Achmad, Andani; Hazriani, Hazriani
Simtek : jurnal sistem informasi dan teknik komputer Vol. 10 No. 1 (2025): April 2025
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v10i1.1530

Abstract

Penelitian ini menyoroti tingginya angka kejadian malformasi uterus yang berkontribusi terhadap infertilitas, keguguran, serta komplikasi perinatal, sehingga diperlukan metode diagnostik yang lebih presisi. Tujuan utama dari penelitian ini adalah merancang model deep learning berbasis Convolutional Neural Network (CNN) dengan arsitektur ResNet untuk mendeteksi berbagai jenis malformasi uterus melalui citra Histerosalpingografi (HSG) serta menilai tingkat akurasinya dalam mengklasifikasikan enam jenis malformasi, yaitu unicornuate, bicornuate, didelphys, septate, arcuate, dan uterus normal. Dataset yang digunakan mencakup 1.800 citra yang terbagi secara merata ke dalam enam kategori. Model ResNet Baseline menunjukkan performa terbaik dengan tingkat akurasi, presisi, recall, dan F1-Score sebesar 100% pada data latih sebesar 90%.
ANALISIS BERITA KRIMINAL BERBASIS GRAPH CLASSIFICATION Jasman, Jasman; Hazriani, Hazriani; Yuyun, Yuyun
Simtek : jurnal sistem informasi dan teknik komputer Vol. 10 No. 1 (2025): April 2025
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v10i1.1532

Abstract

Analisis berita kriminal berbasis klasifikasi graf merupakan pendekatan inovatif yang menggunakan representasi graf untuk memahami hubungan antar entitas dalam data kriminal. Penelitian ini bertujuan untuk mengembangkan model klasifikasi berbasis Deep Graph Convolutional Neural Networks (DGCNN) untuk berita kriminal, mengevaluasi efektivitasnya, dan menghasilkan analisis prediktif yang mendukung penegakan hukum. Metode penelitian dimulai dengan pengumpulan data dari 1.500 berita kriminal, yang diproses melalui tahapan preprocessing seperti tokenisasi dan filtering untuk menghasilkan graf yang merepresentasikan hubungan antar entitas. Selanjutnya, model DGCNN dilatih menggunakan dataset ini, dengan metrik akurasi, precision, dan recall sebagai indikator kinerjanya. Hasil penelitian menunjukkan bahwa DGCNN mampu menangkap pola-pola kompleks, seperti keterkaitan antara pelaku, korban, lokasi, dan waktu kejadian. Namun, terdapat kendala pada overfitting dan underfitting, terutama pada dataset dengan distribusi yang tidak seimbang. Kesimpulannya, meski DGCNN menunjukkan potensi signifikan dalam analisis kriminal, peningkatan pada teknik regulasi, augmentasi data, dan pemilihan parameter diperlukan untuk memaksimalkan generalisasi model, sehingga mendukung prediksi kriminal yang lebih akurat dan strategis.
Rekomendasi Strategi Sosialisasi Program Studi Melalui Jalur Undangan Menggunakan Algoritma ID3 dan K-Means Hairudin, Muhammad Azhar; Zainuddin, Hazriani; Wabula, Yuyun
JITCE (Journal of Information Technology and Computer Engineering) Vol. 6 No. 01 (2022)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.6.01.14-18.2022

Abstract

Based on data obtained from SPAN-PTKIN registrants in 2018 and 2019, the number of interested people through the invitation path who chose the study program at UIN Alauddin as the first choice was 30523 records. Analysis using the ID3 algorithm found that those who interested in the study of religions were more dominant from vocational students. While analysis using the K-Means shows the regions / regencies from which those interested in study programs of religions are spread in 35 regencies / cities. It can be concluded that the socialization of study programs of religions through the invitation path is recommended to be more focused on SMAs that are located in 33 districts / cities as identified in cluster 3. The study programs of religions are prioritized, because these study programs experienced the lowest number of registrants. It is expected that by implementing this recommended strategy, the number of interested prospective new students will draw a significant increase in the future.
Pemberdayaan Kelompok Sadar Wisata Desa Pancana Kabupaten Barru Dalam Mengembangkan Potensi Pariwisata Melalui Penerapan Tata Kelola Dan Promosi Berbasis Digital Razak, Mashur; Hazriani, Hazriani; Firman, Ahmad
Nobel Community Services Journal Vol 5 No 1 (2025): Nobel Community Services Journal
Publisher : Lembaga Penelitian, Publikasi dan Pengabdian Masyarakat ITB Nobel Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37476/ncsj.v5i1.5226

Abstract

Kegiatan pengabdian pada masyarakat ini bertujuan untuk membantu mitra dalam hal ini kelompok sadar wisata Desa Pancana dalam mengatasi permasalahan yang dihadapi dengan memberikan pelatihan dan pendampingan tata kelola bisnis pariwisata dan keterampilan memasarkan produk pariwisata melalui platform digital. Sebagai realisasi dari kegiatan ini, telah dilakukan pelatihan dan pendampingan bagi 20 orang anggota pokdarwis, penandatangan MoU dengan PHRI dan ASITA setempat, pengaktifan plaform sosial media untuk media promosi dan perumusan paket wisata unggulan Desa Pancana
Co-Authors A Muh Fajar Maulana Natsir A. Ade Rosali Saputra Abdul Latief Arda Abdul Rokhman Abdul Rokhman Adnan Nur Agussalim, Agussalim Ahmad Firman Ahmad, Andani Andani Achamad Andani Achmad Andani Achmad Andi riski ramadani APRILIANI, AULIA Aris Susanto Ayu Hasnining Baital, Muhammad Syarif Basirung Umaternate BASRI BASRI Dedi Suarna Dhimas Tribuana Elsa Sera Fajar Husain Asy'ari Hafsah HS Hafsah. HS Hairudin, Muhammad Azhar harahap , Nurlina Ariani Harahap, Amin Hasanuddin, Zulfajri Herlina Ahmad herlinah herlinah Herlinah Herlinah Imam Akbar, Imam Imam T. Umagapi Ingrid Nurtanio Ita Sarmita Samad, Ita Sarmita Jasman Jasman La Ija Latied Arda, Abdul Leonard Maramis Lut Faizal M, Muhtadin Mashur Razak Muh Arfah Wahlil Pratama Muhammad Alam, Ridho Muhammad Arfah Wahlil Muhammad Azhar Hairudin MUHAMMAD FUAD Muhammad Ikhwan Mardin Munthakhabah. R, Cita St. Musliadi K H Nasrullah Nasrullah Nur, Adnan Nuradha, Nuradha Nurul Azmi Omar Wahid Purnamasari, Etika riswanto riswanto Salmawati Samasil Samsart Deandi Palumery SYAMSU ALAM Valensa Yosephi Wabula, Yuyun Wibowo, Deni Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun, Yuyun Zahir Zainuddin Zahir Zainuddin