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All Journal Dinamik GEMA TEKNOLOGI Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Syntax Jurnal Informatika Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Mahasiswa FEB Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Berkala Epidemiologi Seminar Nasional Informatika (SEMNASIF) CESS (Journal of Computer Engineering, System and Science) E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Ilmiah FIFO Jurnal Pilar Nusa Mandiri InComTech: Jurnal Telekomunikasi dan Komputer Prosiding Seminar Nasional Teknoka JRST (Jurnal Riset Sains dan Teknologi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jurnal Telematika STRING (Satuan Tulisan Riset dan Inovasi Teknologi) CCIT (Creative Communication and Innovative Technology) Journal Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Ilmu Komputer dan Bisnis Syntax Idea Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Mnemonic Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi Journal of Computer Science and Engineering (JCSE) SKANIKA: Sistem Komputer dan Teknik Informatika Media Gizi Kesmas Jurnal Teknik Informatika (JUTIF) Jurnal Pewarta Indonesia JURNAL KOMUNIKASI DAN BISNIS Ascarya: Journal of Islamic Science, Culture and Social Studies Jurnal PkM (Pengabdian kepada Masyarakat) Humantech : Jurnal Ilmiah Multidisiplin Indonesia Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Journal Of Human And Education (JAHE) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Algoritma Jurnal Ticom: Technology of Information and Communication Berita Kedokteran Masyarakat Journal of Systems Engineering and Information Technology J-Icon : Jurnal Komputer dan Informatika Jurnal Teknik Indonesia Research Horizon Jurnal Relawan dan Pengabdian Masyarakat REDI Jurnal Pengabdian Masyarakat Nasional Health Dynamics Jurnal Ticom: Technology of Information and Communication The Indonesian Journal of Computer Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Prosiding SeNTIK STI&K Journal of Medical and Health Science Jurnal Ilmu Kesehatan Immanuel Jurnal Analogi Hukum
Arief Wibowo
Universitas Budi Luhur
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Sistem Penunjang Keputusan Pemilihan Pegawai Penerima Promosi Menggunakan Metode Ahp Dan Topsis Siregar, Sutan Syahdinullah; Wibowo, Arief
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 5, No 1 (2021): SEMNAS RISTEK 2021
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v5i1.5070

Abstract

Pegawai pada suatu kantor pemerintahan merupakan ujung tombak dalam pelayanan. Pegawai yang memiliki kinerja dalam pelayanan maupun pekerjaannya berpeluang mendapatkan promosi atau peningkatan posisi. Kegiatan manajemen promosi pegawai bukan sekedar memberikan suatu jabatan atau eselon baru, melainkan juga sebagai motivasi bagi pegawai lain. Badan Kepegawaian Pendidikan dan Pelatihan Kota TangerangSelatan (BKPP Kota Tangsel) merupakan Perangkat Daerah yang mempunyai tugas dan fungsi adalah mengelola sumberdaya manusia. Tidak jarang sering terjadi masalah dalam pengelolaannya, seperti proses pemilihan kandidat pegawai yang belum terstandar dengan baik walaupun telah memiliki beberapa kriteria. Untuk itu diperlukan sistem penunjang keputusan yang dapat membantu pihak-pihak pengambil keputusanagar dapat memperluas kapabilitas mereka dalam menganalisis situasi pemilihan promosi jabatan. Penelitian ini menggunakan metode AHP (Analytical Hierarchy Process) untuk mencari nilai bobot kriteria dan metode TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) untuk mencari hasil akhir berupa ranking alternatif. Hasil penelitian menunjukkan bahwa sistem dapat memberikan kemudahan dan ketepatandalam menentukan pemilihan karyawan yang layak dipromosikan dan menampilkan rangking untuk setiap alternatif berdasarkan hasil preferensi yang ada.
Sosialisasi Pemanfaatan Aplikasi Log Aktifitas Potensi SAR (AplotSAR) Sebagai Teknologi Pendukung Operasi Pencarian dan Pertolongan Probo Anggraini, Julaiha; Haris Achadi, Abdul; Wibowo, Arief; Marlina, Hesti
Journal Of Human And Education (JAHE) Vol. 4 No. 4 (2024): Journal Of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v4i4.1232

Abstract

Kolaborasi antara Badan SAR Nasional (BASARNAS) dengan potensi SAR telah terbukti efektif dalam menangani berbagai keadaan darurat di Indonesia. Meskipun demikian, tantangan dalam koordinasi dan pencatatan aktivitas di lapangan masih ada. Program pengabdian masyarakat ini bertujuan untuk mengatasi tantangan tersebut melalui pengembangan dan pemanfaatan Aplikasi Log Aktivitas Potensi SAR (APLOTSAR). Metode yang digunakan meliputi sosialisasi dan pelatihan penggunaan aplikasi APLOTSAR bagi para rescuer dan potensi SAR. Hasil kegiatan menunjukkan peningkatan pemahaman dan keterampilan peserta dalam menggunakan teknologi ini, serta peningkatan efisiensi dan efektivitas operasional tim SAR. Penggunaan APLOTSAR memungkinkan pencatatan dan pelaporan aktivitas secara real-time, yang pada gilirannya meningkatkan koordinasi dan respons dalam situasi darurat.
Moran’s Index Spatial Analysis On The New Case Detection Rate of Leprosy in West Java 2022 Jasmine, Meuthia; Wibowo, Arief
Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Vol. 7 No. 6: JUNE 2024 - Media Publikasi Promosi Kesehatan Indonesia (MPPKI)
Publisher : Fakultas Kesehatan Masyarakat, Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/mppki.v7i6.5317

Abstract

Introduction: Leprosy is a chronic infectious disease, neglected tropical disease caused by Mycobacterium leprae. Leprosy is divided into two types based on the number of lesions on the skin. If there are less than 5 lesions, leprosy is classified as paucibacillary (PB) and if there are more than 5 lesions, leprosy is classified as multibacillary (MB). Indonesia is ranked third in the world after Brazil with 762 new cases of grade 2 disability leprosy by 2022. Therefore, spatial analysis of the new case detection rate of leprosy in West Java 2022 is needed. Objective: To determine wheter there are spatial dependency with the new case detection rate of leprosy in West Java 2022 Method: Quantitative research that utilises secondary data by conducting spatial analysis on the new case detection rate of leprosy in West Java 2022 using Moran's Index and LISA tests. Result: There are 5 areas namely Bekasi, Karawang, Subang, Indramayu, and Cirebon that have not yet reached the national target in eliminating leprosy. The value of Moran's I = 0.241 and p-value 0.0090 < ? (0.05) which means that there is a weak positive spatial dependence on the number of new leprosy cases in West Java Province 2022. There is 1 region that is in the high-high quadrant, namely Cirebon and there are 4 regions in the low-low quadrant, namely Cimahi City, Bandung City, Garut, and Tasikmalaya. Conclusion: A small number of areas in West Java Province still have not reached the national target of eliminating leprosy, which is a CDR of <5 per 100.000 population, namely Bekasi, Karawang, Subang, Indramayu, and Cirebon. There are 5 areas that have spatial linkages in the new case detection rate of leprosy in West Java Province in 2022 based on the significance value. The region in the high-high quadrant is Cirebon and there are 4 regions in the low-low quadrant, namely Cimahi City, Bandung City, Garut District, and Tasikmalaya District.
Analisis Sentimen Multi-Aspek Berbasis Konversi Ikon Emosi dengan Algoritme Naïve Bayes untuk Ulasan Wisata Kuliner Pada Web Tripadvisor Azzahra, Sitti Aliyah; Wibowo, Arief
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 4: Agustus 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020731907

Abstract

Wisatawan seringkali mencari informasi tentang obyek wisata pada situs web seperti TripAdvisor. Situs web TripAdvisor memiliki fitur bagi penguna terdaftar untuk memberi ulasan tentang objek wisata dalam kategori kuliner dari berbagai negara. Ulasan tersebut bisa digunakan wisatawan sebagai pertimbangan sebelum mendatangi objek wisata kuliner yang ingin dituju. Komentar atau ulasan yang ada di situs TripAdvisor dapat dianalisis untuk mengetahui nilai sentimen dari suatu obyek wisata yang diulas. Hasil analisis itu dapat bermanfaat bagi pengelola tempat wisata, pengusaha kuliner maupun bagi wisatawan lain. Ada tantangan yang ditemukan saat analisis sentimen dilakukan pada kalimat ulasan yang mengandung ikon emosi atau emoticon, karena ulasan dapat mengandung arti sentimen yang berbeda antara kalimat dengan ekspresi emosi yang ada. Penelitian ini berisi analisis ulasan tentang kuliner kota Bandung pada situs TripAdvisor yang mengklasifikasi sentimen menjadi tiga kelas. Penelitian ini menggunakan teknik klasifikasi data mining dengan algoritme Naïve Bayes dikombinasi dengan metode pelabelan multi aspek yang disertai konversi ikon emosi pada teks ulasan. Selain itu, analisis dilakukan pada bobot ulasan berdasarkan jumlah kontribusi pemberi ulasan di web TripAdvisor. Hasil pengujian menunjukkan bahwa penggunaan seluruh kombinasi metode tersebut dalam proses klasifikasi sentimen mampu menghasilkan nilai akurasi sebesar 98,67%. AbstractTourists often look for information about attractions on websites such as TripAdvisor. The TripAdvisor website has a feature for registered users to provide reviews about attractions in the culinary category from various countries. These reviews can be used by tourists as a consideration before visiting culinary attractions to be addressed. Comments or reviews on the TripAdvisor site can be analyzed to determine the sentiment value of a tourist attraction being reviewed. The results of the analysis can be useful for managers of tourist attractions, culinary entrepreneurs and for other tourists. There are challenges that are found when sentiment analysis is carried out on review sentences that contain emotion icons or emoticons, because reviews may contain different sentiment meanings between sentences and existing emotional expressions. This study contains a review of the culinary analysis of the city of Bandung on the TripAdvisor site which classifies sentiments into three classes. This study uses data mining classification techniques with the Naïve Bayes algorithm combined with a multi-aspect labeling method accompanied by the conversion of emotional icons in the review text. In addition, the analysis is carried out on the weight of the review based on the number of contributing reviewers on the TripAdvisor web. The test results show that the use of all combinations of these methods in the sentiment classification process is able to produce an accuracy value of 98.67%.
Prediksi Jumlah Pengiriman Barang Menggunakan Kombinasi Metode Support Vector Regression, Algoritma Genetika dan Multivariate Adaptive Regression Splines Nendi, Nendi; Wibowo, Arief
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722441

Abstract

Sektor usaha logistik telah berkembang sangat pesat di Indonesia saat ini. PT. XYZ  adalah sebuah perusahaan logistik yang menyediakan jasa pengiriman barang dari satu tempat menuju ke tempat yang lain. Sebagai perusahaan logistik dengan jumlah kendaraan 2.100 unit armada truk dan akan terus bertambah seiring dengan target yang dicanangkan perusahaan, dimana pada 2020 jumlah armada truk harus mencapai 6.000 unit truk. Saat ini strategi operasional logistik dihasilkan berdasarkan pengalaman dari steakholder. Hal ini tentu tidak bisa dipertanggung jawabkan secara ilmiah. Prediksi jumlah pengiriman barang harian dapat menjadi solusi dalam membantu perusahaan dalam merencanakan, memonitoring dan mengevaluasi strategi operasional logistik. Hasil pengujian menunjukkan penggabungan metode Support Vector Regression (SVR), algoritma genetika dan Multivariate Adaptive Regression Splines (MARS) dapat menghasilkan prediksi jumlah pengiriman barang harian dengan nilai Mean Absolute Percentage Error (MAPE) yaitu 0.0969% dengan parameter epsilon(????) 1.92172577675873E-20, complexitas(????) 62 dan gamma(γ) 1.0. AbstractThe logistics business sector has developed very rapidly in Indonesia today. PT XYZ is a national logistics company that provides freight forwarding services from one place to another. As a national-scale logistics company, the company is supported by a fleet of 2,100 trucks. The number of fleets will continue to grow in line with the target set by the company, namely in 2020 the number of truck fleets must reach 6,000 trucks. Currently the logistics operational strategy is produced based on stakeholder experience, this certainly causes problems in the company's overall operations. Prediction of the number of daily goods shipments can be a solution in helping companies in planning, monitoring and evaluating logistical operational strategies, based on the company's ability in the availability of a fleet of vehicles for shipping. This study proposes a combination of Support Vector Regression (SVR) methods, genetic algorithms and Multivariate Adaptive Regression Splines (MARS) for problem solving in the prediction process, including in the selection of appropriate training data. The test results show that the combination of the three methods can produce predictions of the number of daily shipments with values of Mean Absolute Percentage Error (MAPE) 0.0969%, epsilon (????) 1.92172577675873E- 20, complexity (????) 62, and gamma (γ) 1.0.
Segmentasi Pelanggan Ritel Produk Farmasi Obat Menggunakan Metode Data Mining Klasterisasi Dengan Analisis Recency Frequency Monetary (RFM) Termodifikasi Wibowo, Arief; Handoko, Andy Rio
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 3: Juni 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020702925

Abstract

Secara umum, pembelian produk farmasi di Indonesia tidak memiliki pola. Pembelian produk farmasi seperti obat-obatan, dilakukan oleh individu bukan sebagai persiapan untuk menjaga kesehatan, namun sebagai respon terhadap penyakit yang sedang diderita. Di sisi lain, pelanggan retail produk farmasi obat biasanya dipengaruhi oleh faktor harga jual dan faktor kecocokan (sugesti) pada merk obat tertentu sewaktu melakukan pembelian. Berdasarkan kondisi itu maka pola pembelian obat bagi masyarakat Indonesia menjadi tidak dapat diprediksi. Hal tersebut membuat pelaku usaha di bisnis ritel produk farmasi obat, relatif sulit untuk meningkatkan nilai penjualan. Salah satu upaya yang bisa dilakukan pelaku bisnis untuk meningkatkan pendapatan adalah dengan melakukan promosi penjualan berdasarkan jenis kelompok pelanggannya. Transaksi pembelian produk farmasi obat dapat dianalisis untuk mengetahui segmentasi pelanggan berdasarkan pola pembelian. Riset ini telah berhasil memodelkan segmentasi pelanggan ritel apotek dengan teknik data mining klasterisasi. Metode yang digunakan adalah melakukan analisis data transaksi pembelian yang terdiri dari atribut Recency Frequency Monetary (RFM) termodifikasi. Analisis telah melibatkan atribut Kuantitas (Quantity) dari data transaksi pembelian produk farmasi obat sebagai eksperimen modifikasi model. Pada proses pemodelan klasterisasi, studi ini menggunakan algoritme data mining K-Means. Hasil penelitian menunjukkan bahwa segmentasi pelanggan yang optimal berada pada dua klaster berdasarkan hasil analisis QRF (Quantity, Recency dan Frequency) menggunakan evaluasi Davies Bouldin Indeks (DBI) dengan nilai 0,527. Kinerja model tersebut dibandingkan dengan algoritme K-Medoids. Hasil klasterisasi pelanggan pada dua kategori menggunakan K-Medoids memiliki nilai DBI sebesar 1.334. Berdasarkan nilai pembanding tersebut maka metode K-Means terbukti lebih baik dalam pembentukan klaster pelanggan ritel farmasi obat pada analisis atribut Quantity, Recency dan Frequency.;AbstractIn general, the purchase of pharmaceutical products in Indonesia has no pattern. The purchase of pharmaceutical products such as medicines, made by individuals not as preparation for maintaining health, but in response to the illness being suffered. On the other hand, retail customers of pharmaceutical drug products are usually influenced by selling price factors and suggestions for certain drug brands when making a purchase. Based on these conditions, the pattern of purchasing drugs for Indonesian people is unpredictable. This makes businesses in the retail business of pharmaceutical drug products, relatively difficult to increase sales value. One effort that businesses can do to increase revenue is to conduct sales promotions based on the type of customer group. Drug pharmaceutical product purchase transactions can be analyzed to determine customer segmentation based on purchase patterns. This research has successfully modeled the pharmacy retail customer segmentation with clustering data mining techniques. The method used is to analyze the purchase transaction data consisting of modified Recency Frequency Monetary (RFM) attributes. Analysis has involved the Quantity attribute (Quantity) of the transaction data of pharmaceutical drug product purchases as a model modification experiment. In the cluster modeling process, this study uses the K-Means data mining algorithm. The results showed that the optimal customer segmentation was in two clusters based on the results of the QRF (Quantity, Recency and Frequency) analysis using the Davies Bouldin Index (DBI) evaluation with a value of 0.527. The performance of the model is compared with the K-Medoids algorithm. The results of customer clustering in two categories using K-Medoids have a DBI value of 1,334. Based on these comparative values, the K-Means method is proven to be better in forming pharmaceutical drug retail customer clusters with analysis Quantity, Recency and Frequency attributes.
Comparison of C4.5 Algorithm and Support Vector Machine in Predicting the Student Graduation Timeliness Mailana, Agus; Putra, Andi Agung; Hidayat, Sarifudlin; Wibowo, Arief
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.608

Abstract

In higher educational institutions, graduation rates are one of the many aspects to assess the quality of the learning process. Al-Hidayah Islamic University in Bogor is one of the established private Islamic universities to create skilled human resources with moral values required by many companies nowadays. Having another institution in Bogor as a competitor with the same direction and objective is a challenge for Al-Hidayah Islamic University. Thus a solution is required to face the competition. One solution is to predict the student graduation timeliness of the students using data mining method with classification function. The implemented methodology in the data mining is Discovery Knowledge of Database (KDD), starting from selecting, preprocessing, transformation, data mining, and evaluation/ interpretation. There were two Algorithm models used in this paper, namely C4.5 and Support Vector Machine (SVM). The classification procedure consists of predictor variables and one of the target variables. Predictor variables are gender, Grade Point Average, marital status, and job status. Rapid Miner software was used to process the data. The final results of both Algorithms show an 81% precision rate and 80% accuracy level for the C4.5 Algorithm, while SVM has an 88% precision rate and 85% accuracy level.
APPLICATION OF MACHINE LEARNING IN PREDICTING EMPLOYEE DISCIPLINE VIOLATIONS IN FINANCIAL SERVICE COMPANY Muhamad Fadel; Kanasfi, Kanasfi; Wibowo, Arief
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1229

Abstract

Employee compliance is a commitment to comply with regulations and stay away from matters that are prohibited in the laws and or company regulations which if not obeyed, then employees are given disciplinary sanctions. Employee discipline is an obligation and willingness of employees in obeying all existing rules in a company to achieve its vision and mission, a high-level employee disciplinary violation rate of 38% at PT. HCI who are engaged in financial service sector can have a negative impact on a company's reputation, meanwhile a low level of employee disciplinary violations in a company can have a positive impact on the company's reputation.This paper aims to predict the possibility of employees committing discipline violations and evaluating the performance of accuracy by using Machine Learning Random Forest, Decision Tree, and Naive Bayes techniques. The test results prove that the Machine Learning Random Forest technique is the best model with the highest value in terms of accuracy with a value of 87.30%, while the Machine Learning Decision Tree and Naive Bayes technique has a value of 83.28%and 70.27% respectively, the value from each of the Machine Learning techniques, the comparison was made using majority voting techniques, so as to produce a total accuracy value of 85.31%.With this high accuracy value, the Random Forest model is proven to have better performance individually in analyzing the prediction of disciplinary violations in the application of human resources at company, while the total accuracy value uses a majority voting model of 85.31%, slightly decreased due to the high level of accuracy of the Naïve Bayes model compared to other algorithm models.
SENTIMENT ANALYSIS OF ICT SERVICE USER USING NAIVE BAYES CLASSIFIER AND SVM METHODS WITH TF-IDF TEXT WEIGHTING Trisnawati, Wulan; Wibowo, Arief
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.1784

Abstract

Pusintek is one of the government units in Indonesia responsible for managing Information and Communication Technology (ICT), providing various ICT services to users in central and regional offices through the ICT Service Catalog. The level of service fulfillment in Pusintek's IT Service Catalog significantly influences the effectiveness and efficiency in meeting service agreements, providing accurate information, and handling disruptions promptly. User satisfaction is measured through surveys to plan improvements to ICT services, but there is currently no method to classify sentiment from survey comment data. This research aims to classify sentiment and understand customer opinions and satisfaction trends regarding ICT services. The study applies the Naïve Bayes Classifier and Support Vector Machine (SVM) methods to classify positive and negative comments in user satisfaction surveys of ICT services. The data used consists of comments from the 2022 ICT user satisfaction survey results. Based on the test results, it is observed that the SVM algorithm provides higher accuracy compared to the Naïve Bayes algorithm. Utilizing the existing dataset with established opinion values, classification modeling using Naïve Bayes Classifier and Support Vector Machine (SVM) proves capable of classifying ICT user sentiment into 3 sentiment classes: Positive, Neutral, and Negative. From the data above, it is concluded that the SVM algorithm achieves the highest accuracy of 88.76%, highest precision of 89.68%, recall of 88.76%, and an f1-score of 89.12%.
OPTIMIZATION PRODUCT RECOMMENDATION USING K-MEANS, AGGLOMERATIVE CLUSTERING AND FP-GROWTH ALGORITHM Huda, Ratu Najmil; Fitriadi, Rifqi; Wibowo, Arief
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1901

Abstract

The growth of online business has been rising considerably in recent years. The growth is affected by technology advancement in Internet and smartphones and consumer behavior change for better online shopping experience. To anticipate this swift customer behavior, business owners need to have an excellent inventory management to be able to keep making profits. In data mining realm, the algorithm model that is known to be applied in this case is the association algorithm. This model will explicate customers’ purchasing patterns where is useful in calculating stock accurately. The aim of this research is to find an appropriate model in handling large data to obtain valid association rules that have minimum support value, confidence value, and high lift ratios. It is hoped that the results of this research can provide recommendations for online sellers to manage a large variety of goods and to keep making profits. Datasets that contain a large variety of goods are handled first by using a clustering algorithm to group similar items together. The dataset tested was divided into three groups, namely, dataset without clusters, k-means cluster, and agglomerative cluster. After forming three groups of datasets, FP-Growth was applied to each dataset. The result is that datasets with clusters, whether using k-means or agglomerative, have a minimum support value that is greater than datasets without clusters. Most association rules are obtained from the k-means cluster dataset. Based on the model applied in this research, the association itemset size only obtains one conclusion from one premise.
Co-Authors - Arientawati - Sumardianto Adita, Ita Afifah Khaerani Afifatussalamah, Rizka Ahmad Sururi Ahmad Sururi Akbar, Ahmad Aldizar Al Fatach, M Khabib Anggraini, Julaiha Probo Anita Diana Anugrah Sandy Yudhasti Apriati Suryani Ardhianto, Angga Ardianah, Eva Ari Wibowo Arief Umarjati Asep Permana Atik Ariesta Bayu Sadewo Bayu Satria Pratama Binarto, Antonius Jonet Boerhan Hidayat, Boerhan Danar Wido Seno Darki, Ni Wayan Yustika Agustin Diah Indriani Didik Hariyadi Raharjo Didin Muhidin Dwi Kristanto Dwi Yulianti Dyah Retno Utari Dyah Retno Utari, Dyah Retno Endah Sarah Wanty Fajar Siddik Chaniago Farah Chikita Venna Farid Setiawan Farid Setiawan, Farid Febrilliani, Jihan Sastri Fenny Irawati Fernando, Donny Firman Noor Hasan Firmanty Mustofa, Vina Fitri Nur Masruriyah, Anis Fitri Rachmilah Fadmi Fitriadi, Rifqi Fitriani, Netty Fransiska Vina Sari Frenda Farahdinna Fried Sinlae Ghapur, Abdul Gurdani Yogisutanti Hadidtyo Wisnu Wardani Hananto, Agustia Handoko, Andy Rio Hanindita, Meta Herdiana Hari Basuki Notobroto Haris Achadi, Abdul HARIYANTO HARIYANTO Harun Nasrullah Hayatul Khairul Rahmat Henry Henry Hidayat, Manarul Hidayat, Sarifudlin Huda, Ratu Najmil I MADE MINGGU WIDYANTARA, I MADE MINGGU Indah Rizky Mahartika Inge Virdyna Irfan Hadi Irfan Nurdiansyah Istiqoomatun Nisaa Iwan Irawan Jasmine, Meuthia Joko Sutrisno Jovansgha Avegad Jumaryadi, Yuwan Kanasfi, Kanasfi Karma, Ni Made Sukaryati Karyaningsih, Dentik Kresno Yulianto KRESNO YULIANTO KUNTORO Kuntoro Kuntoro Kurnia Setiawan Kutanto, Haronas Larasati, Pamela Linda Lingga Desyanita Luthfi Akbar Ramadhan Mahmudah Mahmudah Mailana, Agus Maria Adiningsih Marlina, Hesti Maskur A, Moch Riyadi Megananda Hervita Permata Sari Megawati, Rina Miftahul Arifin Miftahul Arifin Mochammad Rizky Royani Moh Makruf Muhamad Fadel Muhammad Febrian Rachmadhan Amri Muhammad Risky Mulyati Mulyati Nendi, Nendi Ningrum, Yogi Ajeng Nugroho, Angelika Pratiwi Widya Nur Aisiyah Widjaja, Nur Aisiyah Nur Rohman Nurcahya, Gelar Nurfidaus, Yasmine Nursyi, Muhamad Pattipeilohy, William Frado Pattipeilohy, William Frado Pebriaini, Prisma Andita Poppy Ruliana Pradiptha, Anindya Putri Probo Anggraini, Julaiha Purwadi Purwadi Putra, Andi Agung Putra, Rinaldi Febryatna Duriat Rachmah Indawati Rahman, Reza Rahmawati, Nur Anisah Rakhman, Abdulah Rakhmat Rakhmat Rakhmat Rakhmat RAMAYU, I Made Satrya Rangkuti, Muhammad Yusuf Rizqon Ratna Ayu Sekarwati Ratna Ayu Sekarwati Relawanto, Bowo Ria Puspitasari Rika Nurhayati Riki Ramdani Saputra Rina Megawati Ririh Yudhastuti Risaychi, Diva Ajeng Brillian Ristiana, Ina Riza, Yeni Rizkiyanto, Muhamad Ardiansyah Roedi Irawan Rojakul, Rojakul Rosita Dewi, Erni Ruliana, Poppy Rusdah Ryo Tanaka Sabirin, Sahril Sadewo, Bayu Santoso, Febrina Mustika Saptari Wijaya Mulia Sari Anggar Kusuma Melati Sari, Fransiska Vina Sasongko, Raden Satiri Satiri, Satiri Selly Rahmawati Selly Rahmawati Septian Firman S Sodiq Septiani, Riska Setya Haksama Setyowati, Erlin Shofinurdin Shofinurdin Siddik Chaniago, Fajar Sigit Ari Saputro Sigit Budi Nugroho Siregar, Sutan Syahdinullah SITI NURUL HIDAYATI Sitti Aliyah Azzahra Soenarnatalina Melaniani Sudewo, Andika Hasbigumdi Sugiyarta, Ahmad Sujiharno Sujiharno Sumarna, Presma Dana Scendi Suntoro, Dimas Fahmi Tiaharyadini, Rizka TRISNAWATI, WULAN Tulus Yuniasih Umam, Mohamad Hafidhul Vasthu Imaniar Ivanoti Wahyu Cesar Wahyu Desena Wahyudi, Widi Wahyuni, Chatarina Unggul Wangsajaya, Yosia Heartha Dhalasta Wasis Budiarto Wibiyanto, Alif Dewan Daru Widiyaningrum, Diyah Kiki Widyanto, Tetrian Windhu Purnomo Yahya Darmawan Yudanto, Satyo Zakaria Anshori Zaqi Kurniawan