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Penerapan Algoritma FP-Growth Untuk Mengetahui Pola Pada Data Transaksi Percetakan (Studi Kasus Java Printing Batujajar) Sugianto, Castaka Agus; Sukmawati, Dini
Journal of Information Technology Vol 5 No 1 (2023): JOINT (Journal of Information Technology)
Publisher : LPPM STMIK AMIK BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Transaction data in the printing industry provides valuable insights into concurrently favored products by customers within a specific timeframe. Java Printing Batujajar has recorded printing transactions over the past 2 years. However, the lack of data management has led to disorganized raw material inventory, resulting in many unprocessed transaction records. The collected data consists of 704 printing transaction records from 2021 to 2022. Consequently, a solution is needed to identify patterns within this transaction data. This research aims to employ the association method with FP-Growth algorithm using the RapidMiner software. The research findings reveal an association between two products: "Buku Yasin L 208hl" (Complete/using elbow and tassel ribbon, 208 pages) and "Buku Yasin L 224hl" (Complete/using elbow and tassel ribbon, 224 pages). Both products exhibit the highest support value, accounting for 11.5% of total transactions, indicating their frequent simultaneous purchases. Moreover, the confidence value for the association between these two products reaches 100%, implying that if a customer purchases "Buku Yasin L 208hl," they will certainly buy "Buku Yasin L 224hl" as well. The test results indicate that by adjusting experimental parameters, higher support and confidence values are achieved compared to using default parameters. This signifies that experimental testing is more effective in discovering product associations and providing valuable information for optimizing the arrangement and storage of related raw materials.
Optimasi Algoritma K-Means Menggunakan Metode Elbow Pada Data Penerima Program Keluarga Harapan (PKH) Sugianto, Castaka Agus; Wanaziana, Keny Kirana
Informatics and Digital Expert (INDEX) Vol. 6 No. 1 (2024): INDEX, Mei 2024
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v6i1.1739

Abstract

Program Keluarga Harapan (PKH) merupakan program pemberian bantuan sosial bersyarat kepada keluarga miskin yang ditujukan kepada keluarga penerima manfaat PKH. Melalui PKH, keluarga miskin didorong untuk memiliki akses dan memanfaatkan pelayanan sosial dasar di bidang kesehatan, pendidikan, pangan dan gizi, perawatan, serta pendampingan. Pada kelurahan Cibabat dan kelurahan Cipageran kecamatan Cimahi Utara terdapat 387 data penerima bantuan PKH pada tahun 2022. Namun belum adanya pengolahan data penerima bantuan PKH tersebut sehingga dalam melakukan pendampingan penerima bantuan PKH belum mendapatkan penglompokan yang sesuai dengan riwayat pendidikan. Berdasarkan latar belakang masalah yang telah dijabarkan, penulis tertarik untuk melakukan Clustering menggunakan Algoritma K-means pada data penerima bantuan PKH. Berdasarkan pengujian metode elbow pada algoritma k-means didapat nilai k yang optimal adalah k=3. Pengelompokan dataset yang digunakan menjadi 3 kelompok cluster, diantranya cluster_0 sebanyak 257 data, cluster_1 sebanyak 75 data, dan cluster_2 sebanyak 55 data. Pada cluster_0 di dominasi oleh peserta lulusan SD sebanyak 173 data, untuk cluster_1 di dominasi oleh peserta tidak sekolah sebanyak 40 data, dan untuk cluster_2 di dominasi peserta tidak sekolah sebanyak 48 data. Pada cluster tersebut didapatkan nilai performa berdasarkan rata-rata avg. within centroid distance_cluster_0 adalah 6.720, avg. within centroid distance_cluster_1 adalah 14.373, avg. within centroid distance_cluster_2 adalah 8.496 dan Davies Bouildin Index adalah 0.816. Hasil penelitian ini diharapkan menjadi acuan bagi pengurus sekretariat PPKH dalam melaksanakan pendampingan masyarakat penerima Program Keluarga Harapan.
Film Review Sentiment Analysis: Comparison of Logistic Regression and Support Vector Classification Performance Based on TF-IDF Ramdan, Dadan Saepul; Apnena, Riri Damayanti; Sugianto, Castaka Agus
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9090

Abstract

Film sentiment analysis is a process for evaluating a sentiment value that exists in film reviews, so that positive or negative responses from films can be identified. In this study, a sentiment analysis will be carried out on film reviews on IMBD. The analysis was carried out to find out which reviews were positive and negative from film critics. The method used to carry out sentiment analysis in this study is review analysis and processing with TF-IDF and a positive or negative prediction process based on reviews that have been processed using a logistic regression algorithm and support vector classification. The data to be used is film reviews on IMBD, which consists of 2000 data, which is divided into 1000 positive data and 1000 negative data. Which is where the data will be preprocessed first and split with a percentage of 70% training data and 30% testing data. In the prediction process using the logistic regression algorithm, obtaining a test accuracy of 80.61%. While the prediction process using the support vector classification algorithm obtains a test accuracy of 82.42%.
PELATIHAN PENGGUNAAN FITUR HEADING DAN PENGETIKAN SEPULUH JARI DI MADRASAH ALLIYAH MIFTAHUSSAADAH KOTA CIMAHI Ernawati, Tati; Kosasih, Heldi Akbar; Rohmayani, Dini; Sugianto, Castaka Agus
PUAN INDONESIA Vol. 5 No. 2 (2024): Vol. 5 No. 2 (2024): Jurnal Puan Indonesia Vol 5 No 2 Januari 2024
Publisher : ASOSIASI IDEBAHASA KEPRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37296/jpi.v5i2.186

Abstract

The problem is that the material using the "Heading" feature on the word processor application for automatic content list creation has not been submitted.  Another problem based on the observations is that the students are still not used to typing with ten fingers so it takes a long time when practicing computers.  The method used in the implementation of training activities is to display material continued with practicum. The "Heading" feature complements the material already presented by the teacher of the TIK subject, where the material has not been presented in front of the class.  As a result of ten-finger drawing training, there was an improvement in the students' ability to type although not significantly. The training material is very relevant to the curriculum used by Madrasah Aliyah Miftahussaadah and complement the materials already learned in the learning process by the teacher of the TIK subject. The participants were very enthusiastic during the training, it is proved with the participant's activity during the receipt of the training material.  For the team especially the students give a positive impact to sharpen the softskill skills of the students before plunging into the world of work and industry as well as to society.      
ANALISIS SENTIMEN ULASAN APLIKASI DANA DI GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAÏVE BAYES Riansyah Ramadhan, Gery; Agus Sugianto, Castaka
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 5 (2024): JATI Vol. 8 No. 5
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i5.10732

Abstract

Dalam era digital, aplikasi dompet digital seperti DANA menjadi penting terutama selama pandemi COVID-19 yang meningkatkan transaksi non-tunai. Penelitian ini menganalisis sentimen ulasan aplikasi DANA di Google Play Store menggunakan metode Naïve Bayes Classifier. Data sebanyak 2000 ulasan berbahasa Indonesia dikumpulkan melalui web scraping dan dilabeli sebagai positif, negatif, atau netral berdasarkan rating. Proses penelitian menggunakan metode Knowledge Discovery from Data (KDD) yang melibatkan tahapan seleksi data, preprocessing, pembobotan, klasifikasi, dan evaluasi. Preprocessing dilakukan untuk membersihkan dan menyiapkan data. TF-IDF untuk pembobotan kata, kemudian model Naïve Bayes digunakan untuk klasifikasi sentimen. Hasil evaluasi menggunakan confusion matrix menunjukkan performa model yang baik. Terdapat 1476 ulasan positif, 443 ulasan negatif, dan 81 ulasan netral dengan akurasi 86%, presisi 86%, recall 98%, dan f1-score 92%. Studi ini menyimpulkan bahwa model analisis sentimen ini mampu memberikan prediksi yang akurat, meskipun terdapat variasi dalam nilai presisi, recall, dan f1-score. Kata yang sering muncul pada ulasan negatif terkait dengan masalah masuk aplikasi, transaksi gagal, dan isu pengisian saldo, yang merupakan area yang perlu diperhatikan oleh pengembang aplikasi DANA.
Optimalisasi Algortima Klasifikasi Ensemble Menggunakan Algortima Genetika Untuk Prediksi Resiko Diabetes Febianto, Aldi Kristiawan; Sugianto, Castaka Agus
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 2 (2024): Edisi Juni
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i2.391

Abstract

Diabetes mellitus (DM) is a global health problem that affects the quality of life and life expectancy of patients. Diabetes risk prediction can help in disease prevention and management. This study aims to optimize ensemble classification for diabetes risk prediction using a Genetic Algorithm (Optimize Selection). The Ensemble methods used are Bagging, Random Forest, and AdaBoost. The genetic algorithm is applied for Ensemble model hyperparameter optimization. The data used is the Pima Indians Diabetes dataset which consists of 768 samples with 8 features. Experimental results show that Ensemble Classification optimized with the Genetic Algorithm (Optimize Selection) produces quite good performance. The accuracy of the Genetic Algorithm (Optimize Selection) + Ensemble Bagging Classification Algorithm got a result of 97.14%, the Genetic Algorithm (Optimize Selection) + Random Forest Ensemble Classification Algorithm got a result of 98.57%, and the Genetic Algorithm (Optimize Selection) + AdaBoost Ensemble Classification Algorithm got a result of 99.87%. %, These results indicate that this approach can be an effective solution in diabetes risk prediction.
Optimalisasi Algoritma K-Means Menggunakan Metode Elbow Dalam Pengelompokan Data Stunting Safira, Rifa; Sugianto, Castaka Agus
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 2 (2024): Edisi Juni
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i2.396

Abstract

Stunting is a disease related to malnutrition and results in a lack of growth in toddlers, especially in growing children's height that is not in accordance with their age. Based on observations made by the author at the North Cimahi Health Centre, there are many stunting toddlers, especially in the Cibabat Cimahi North - Cimahi village area. This study aims to optimise the clustering of stunting data using the K-Means algorithm with the elbow method in Cibabat Cimahi Utara Village, the stunting toddler data used is 320. elbow method is used to determine the best number of clusters.  The results of this study indicate that the best cluster from the results of the elbow method is 4: Cluster_0 as many as 98 children under five, Cluster_1 as many as 89 children under five, Cluster_2 as many as 70 children under five, Cluster_3 as many as 63 children under five. Performance value obtained based on average avg. within centroid distance_Cluster_0 as much as 8.844, Cluster_1 as much as 9.793, Cluster_2 as much as 9.818, Cluster_3 as much as 17.726 and Davies Bouldin Index results as much as 0.151. The results of this study can be used as a basis for formulating better policies and suppressing stunting rates in the Cibabat Cimahi Utara Village area
ANALISIS SENTIMEN TIM NASIONAL SEPAK BOLA INDONESIA DI TURNAMEN PIALA DUNIA U-17 INDONESIA PADA TWITTER (X) MENGGUNAKAN ALGORTIMA NAÏVE BAYES Juniardi, Trianda; Sugianto, Castaka Agus
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5188

Abstract

Piala Dunia U-17 adalah salah satu ajang bergengsi dalam sepak bola internasional yang menarik perhatian luas, termasuk di platform media sosial seperti Twitter(X). Penelitian ini bertujuan untuk menganalisis sentimen masyarakat Indonesia terhadap tim nasional sepak bola U-17 Indonesia selama ajang Piala Dunia U-17 menggunakan algoritma Naïve Bayes. Data dikumpulkan dari komentar-komentar Twitter(X) yang diklasifikasikan menjadi sentimen positif, negatif, dan netral. Metode ini melibatkan proses preprocessing untuk membersihkan dan mengelola data teks sebelum analisis. Hasil analisis menunjukkan mayoritas masyarakat mengekspresikan sentimen positif terhadap timnas U-17 Indonesia, dengan dukungan yang kuat terlihat dari kata-kata seperti "timnas," "Indonesia," dan "kebanggaan." Namun, terdapat juga kritik yang signifikan terhadap performa timnas, mencerminkan variasi opini dalam masyarakat. Metode Naïve Bayes berhasil mengklasifikasikan sentimen dengan akurasi 78.34%, presisi 82.96%, recall 78.34%, f1-score 74.95%. Penelitian ini memberikan wawasan penting tentang persepsi dan respons masyarakat terhadap event olahraga besar di Indonesia, serta relevansi dan kegunaan algoritma Naïve Bayes dalam menganalisis data sentimen media sosial secara efektif.
PERBANDINGAN ALGORITMA NAÏVE BAYES DAN K-NEAREST NEIGHBOR (KNN) PADA VIDEO YOUTUBE MENGENAI GLOBAL WARMING Himawan, Aldi; Agus Sugianto, Castaka
Informatics and Digital Expert (INDEX) Vol. 6 No. 2 (2024): INDEX, November 2024
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v6i2.1803

Abstract

Pemanasan global merupakan salah satu tantangan lingkungan utama yang dihadapi dunia saat ini, disebabkan oleh peningkatan emisi gas rumah kaca. Media sosial, khususnya YouTube, menjadi platform penting dalam menyebarkan informasi dan menciptakan kesadaran tentang isu-isu ini. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap video YouTube yang membahas pemanasan global, khususnya video yang dibuat oleh Najwa Shihab. Dua metode klasifikasi digunakan, yaitu Naïve Bayes dan K-Nearest Neighbor (KNN), untuk mengklasifikasikan komentar pengguna menjadi kategori positif atau negatif. Data komentar dikumpulkan menggunakan teknik web scraping pada Februari 2024, dan dianalisis menggunakan metode SEMMA Data Mining Process. Hasil analisis menunjukkan bahwa metode Naïve Bayes dan KNN memberikan kinerja yang kompetitif dalam mengklasifikasikan sentimen komentar-komentar ini. Evaluasi dilakukan dengan membagi dataset komentar menjadi beberapa rasio pelatihan dan pengujian, Evaluasi dilakukan dengan membagi dataset komentar menjadi beberapa rasio pelatihan dan pengujian: 70:30, 80:20, dan 90:10. Hasil analisis menggunakan rasio 90:10 yang sudah dilakukan menggunakan 10-fold cross validation menunjukkan bahwa algoritma Naïve Bayes memperlihatkan kinerja yang kompetitif dengan akurasi sebesar 79.17%, presisi sebesar 80.76% recall sebesar 79.17%, dan f1-score sebesar 78.68%. Sementara itu, K-Nearest Neighbor juga menunjukkan kinerja yang kompetitif, dengan akurasi sebesar 76.06% , presisi sebesar 76.70%, recall sebesar 76.06%, dan f1-score sebesar 75.98% . Hasil pengujian T-Test menujukan perbedaan yang signifikan antara algoritma Naïve Bayes dan K-Nearest Neighbor dengan nilai probabilstik 0.0001 kurang dari nilai alpha yaitu 0.05 yang menunjukan bahwa algoritma Naïve Bayes lebih unggul performanya. Selain itu, analisis sentimen menunjukkan bahwa mayoritas komentar memiliki sentimen negatif, mencerminkan bahwa video tersebut berhasil meningkatkan kesadaran masyarakat mengenai isu pemanasan global dan memicu diskusi yang konstruktif.
Mackerel Tuna Freshness Identification Based on Eye Color Using K-Nearest Neighbor Enhanced by Contrast Stretching and Histogram Equalization Dahlan, Dahlan; Iskandar, Rachmat; Ekawati, Nia; Sugianto, Castaka Agus
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.14494

Abstract

Purpose: The present study focuses on the development of a robust fish freshness classification system based on the application of different digital image processing techniques from mackerel tuna eye images toward better classification. Methods: Contrast stretching and histogram equalization were done to improve image quality before the classification. The system contained 250 training images in a dataset, for five freshness classes which are 3, 6, 9, 12, and 15 hours post-catch, with 50 test images. For classification, the K-Nearest Neighbor (KNN) algorithm was employed with a parameter setting of K = 5, ensuring effective differentiation between the various freshness levels based on the enhanced image features. Result: The results depicted very low MSE values after enhancement at 6 hours, as low as MSE = 0.0012606 and PSNR = 28.9944 dB for contrast stretching, and for 12 hours, histogram equalization gave the best results, MSE = 0.0030712 and PSNR = 25.127 dB. Further, classification done through the KNN classifier with K=5 gave results with accuracy as high as 100% was achieved on the testing data, proving that the model was successfully able to identify the classes of freshness. Novelty: The novelty in the present research work is the integration of advanced image-processing techniques, which allow the achievement of an improved level of detection of fish freshness and a very useful solution to the seafood industry in view of product quality and safety assurance. Generally, the paper epitomizes an important milestone in the application of machine learning and image processing for the assessment of the quality of foods.
Co-Authors Ade Yuliana Aditia Gusman Aldy Noer Tjahyo Apnena, Riri Damayanti Aqmal Mulqy Bagja Laksana Ari Sudrajat Aris Haris Rismayana Arya Aditya Ayu Hendrati Rahayu Candra Irawan Christy Atika Sari Dadan Saepul Ramdan Dahlan Dahlan Dini Rohmayani Eko Hari Rachmawanto Faishal Fachruddin Febianto, Aldi Kristiawan Firdhani Faujiyah Fitrah, La Ode Muhammad Fredricka, Jhoanne Gerinata Ginting Heldi Akbar Kosasih Hidayah, Hamimah Nur Himawan, Aldi Iqna Aulia Iskandar, Rachmat Juniardi, Trianda Kosasih, Heldi Akbar Lela Kesmasari Maulana, Firdi Rizky Melani Nur Astita Mohamed Doheir Muhammad Ridwan Nadya Selvi Pujiyanita Nafea, Mohammed Mansoor Nandhita Ayusari Nia Ekawati, Nia Novita Lestari Anggreini Pebriana, Sela Perdana, Rangga Satria Pujiyanita, Nadya Selvi Rahmawan, Septiadi Ramdan, Dadan Saepul Riansyah Ramadhan, Gery Riyan Riyan Rizqy Dimas Monica Rosdianti, Elfi Safira, Rifa Sari, Putri Nurlaela Shandy Tresnawati Sukmawati, Dini Syaefuloh, Herman Syams, Fawwaz Muhammad Syifa Fauzia Tahir Tahir, Syifa Fauzia Tati Ernawati Taufik Fajar Mustafa Tresnawati, Shandy Tri Herdiawan Apandi Tri Pratiwi Olivia Riska Bokings Wanaziana, Keny Kirana Zulkipli ., Zulkipli Zundi, Teguh Muhammad