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All Journal Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Yuyun Yuyun
Universitas Handayani Makassar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Identifikasi Sampah Plastik Menggunakan Algoritma Deep Learning Lut Faizal; Yuyun Yuyun; Hazriani Hazriani
Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Vol 6 No 2 (2023): Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Lamappapoleonro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57093/jisti.v6i2.176

Abstract

Masalah sampah plastik masih belum terselesaikan secara optimal hingga saat ini. Jumlah sampah yang dihasilkan oleh kegiatan manusia terus meningkat sejalan dengan jumah penduduk yang terus bertambah. Sampah plastik menjadi salah satu jenis sampah yang sulit didaur ulang, terutama jika tercampur dengan sampah lainnya. Agar proses daur ulang menjadi lebih mudah, diperlukan pemilahan sampah berdasarkan jenisnya. Oleh karena itu, sebuah solusi yang bisa diimplementasikan adalah merancang sistem yang menggunakan pendekatan deep learning untuk mendeteksi jenis sampah plastik, berat sampah dan informasi lokasi sampah tersebut. Faster R-CNN merupakan algoritma deteksi objek yang masuk kedalam bidang computer vision berbasis jaringan konvolusi yang dapat digunakan untuk mengindentifikasi sebuah objek. Adapun hasil penelitian yang diperoleh, nilai akurasi sistem yang didapatkan dalam mengidentifikasi botol plastik sebesar 96,3%, gelas plastik 100%, sendok 84,2%, styrofoam 100%, dan undefined 100% sehingga total akurasi sistem yang didapatkan dari 5 objek sebesar 96,3%. Selain itu, sistem juga mampu menampilkan hasil estimasi berat dari objek yang dideteksi sesuai dengan parameter inputan basis data
Penentuan Status Penerima Bantuan Indonesia Pintar pada SMKN 9 Bulukumba Dengan Metode Naive Bayes Muh Arfah Wahlil Pratama; Muhammad Fuad; Hazriani Hazriani; Yuyun Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The Smart Indonesia Program (PIP) through the Smart Indonesia Card (KIP) provides educational cash assistance to school age children (6-21 years). KIP is part of the refinement of the Poor Student Assistance Program (BSM) since the end of 2014. SMKN 9 Bulukumba is located on Jalan Pendidikan No. 57, Tritiro Village, Bontotiro District, Bulukumba Regency. This vocational school is one of the vocational schools in the Bontotiro area that received funds from the Smart Indonesia Program (PIP). The PIP target at SMKN 9 Bulukumba is still not well targeted, due to the lack of criteria for the number of dependents. Therefore, the author added the criteria for the number of dependents in the research. This research was created based on previously existing data, namely 143 training data. Using the Naive Bayes method and with 6 attributes, namely Type of Residence, Number of Dependents, Parent's Occupation, Parent's Income, and KPS Recipient. using RapidMiner's supporting tools in testing the accuracy of the Naive Bayes method. The results of accuracy testing obtained using the RapidMiner application and manual calculations obtained an accuracy of 74.00% and the resulting classification was included in the Good Classification group because the AUC value obtained from testing based on the ROC curve using the Naive Bayes method was 0.860. So, it can be concluded. that the Naive Bayes Algorithm can be applied to determine the feasibility of accepting the Smart Indonesia program for students of SMKN 9 Bulukumba
Prediksi Kelulusan Pegawai Pemerintah Dengan Perjanjian Kerja Guru Menggunakan Metode Naive Bayes Basirung Umaternate; Imam T. Umagapi; Yuyun Yuyun; Hazriani Hazriani
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Recruitment and selection of ASN PNS and PPPK candidates to date has quite a high number of enthusiasts, and the selection process using the Computer Assisted Test (CAT) is the main requirement for passing the PNS or PPPK selection. Therefore, the researcher made this research using pre-existing data or called training data. Researchers used the Naïve Bayes method with 9 mutually independent attributes to determine graduation. Researchers also used Microsoft Excel and Weka supporting applications to test the accuracy of the Naïve Bayes method. Tests were carried out with 212 datasets consisting of 170 training data/training data and 42 test data/testing. The results of the Accuracy, Recall, and Precesion tests determine the graduation of Government Employees with Work Agreements (PPPK) for Teachers in Morotai Island Regency, 100% Accuracy, 100% Precision, 100% Recall, and the Area Under ROC (AUC) value is 1, which means 100% below The curve shows that the performance of the Naïve Bayes algorithm in capitalizing the classification of graduation data for Government Employees with Employment Agreements (PPPK) for Teachers in Puau Morotai Regency is very good.
Implementasi Klasifikasi Naive Bayes Dalam Memprediksi Lama Studi Mahasiswa Muhammad Fuad; Muhammad Arfah Wahlil; Hazriani Hazriani; Yuyun Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Under normal conditions, undergraduate or undergraduate students from a university can complete their studies for 4 years or 8 semesters. In fact, many students complete their study period of more than 4 years. It is known that in the academic year 20XX/20XX there were 161 people who were accepted as students. Of the 161 people admitted, 100 people have completed their study period of about 4 years and the remaining 61 people have completed their studies for 5 years. Based on the problems above, this research implements a classification that can help the university predict the length of study of students who are currently studying in various study programs at the University. The method that the author presents in the classification for predicting the length of a student's study period is the Naive Bayes Algorithm. By using the Java-based Rapid Miner tool to classify graduation data. Then the implementation of data mining which is divided into 136 data training data and 25 data testing data with naive Bayes managed to obtain an accuracy rate of 82% which is also a relatively good parameter.
Klasifikasi Status Gizi Balita Menggunakan Algoritma K-Nearest Neighbor (KNN) Hafsah. HS; Nurul Azmi; Hazriani Hazriani; Yuyun Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Nutrition is an important component contained in food which includes carbohydrates, protein, vitamins, minerals, fat and water which are needed by the body for growth, development and maintenance which are used directly by the body to repair body tissue. Nutritional needs are an important factor in the growth and development of children, especially children aged under five years (toddlers), because what happens in the first five years determines their growth and development year after year. To achieve good growth and development, strong nutrition is needed. Assessment of the nutritional status of toddlers can be determined through human body measurements known as anthropometry. The reference standards for toddler nutritional status are Body Weight according to Age (WW/U) which describes the child's relative weight for age, Body Weight according to Height (WW/TB) which describes whether the child's weight is in accordance with his height growth and Height according to Age (TB/U) describes a child's height growth based on age. The method used to determine the nutritional status of toddlers is the KNN method, which is to find the closest distance between the evaluated data and a number of K neighbors closest to the test data. Toddler nutrition data uses 4 classifications, namely insufficient, normal, poor and more. The amount of data used in this research was 170 data with a data composition of 90% consisting of 150 training data and 10% testing data totaling 20 data. Data that is normalized using Z-Score gets an accuracy of 95%, class precision of 98.08% and class recall of 93.75%, data normalized using the min-max technique gets an accuracy of 85%, class precision of 95.00% and class recall of 79.17%, Meanwhile, KNN modeling without normalization produces an accuracy of 80%, precision of 82.4% and a class recall value of 75%.
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.
Co-Authors A. Ade Rosali Saputra Basirung Umaternate Elsa Sera Hafsah HS Hafsah. HS Hazriani, Hazriani Imam T. Umagapi Lut Faizal Mirfan Muh Arfah Wahlil Pratama Muhammad Arfah Wahlil Muhammad Fuad Nurul Azmi Samsart Deandi Palumery