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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Edulib JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Technomedia Journal Astonjadro J-SAKTI (Jurnal Sains Komputer dan Informatika) Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal Of World Science International Journal Of Science Education and Technology Management (IJSETM) SmartComp The Indonesian Journal of Computer Science
Christina Juliane, Christina
STEI-Institut Teknologi Bandung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

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Optimizing Marketing Strategies Using FP-Growth and Association Rule Mining Algorithms in the Textile Industry NG, Wijaya; Sukma, Robby; Juliane, Christina
Journal of World Science Vol. 3 No. 5 (2024): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v3i5.599

Abstract

This study leverages association rule mining to analyze transaction data from PT. Labda Anugerah Tekstil, a prominent player in the textile industry, to uncover significant purchasing patterns and associations between different fabric types. Utilizing data from January 1, 2022, to December 31, 2023, which includes 7,143 transaction entries, the research applies the FP-Growth algorithm followed by Association Rule Mining to identify and evaluate frequent itemsets and strong association rules within the dataset. The analysis revealed robust associations among fabrics such as Cotton, Linen, Rayon, and Viscose, suggesting substantial opportunities for targeted marketing strategies and inventory management enhancements. The findings indicate that strategically bundling and promoting associated fabrics can drive higher sales volumes and improve customer purchasing experiences. The insights from this study provide actionable strategies for optimizing marketing efforts and inventory management, aiming to enhance sales performance and customer satisfaction in the competitive textile market.
Analysis of the Application of the K-Means Algorithm to the Clustering Method Approach for Grouping Consumer Purchasing Trends at One of the Textile Companies Kurniawan, Debby; Anwari, Hidayat; Juliane, Christina
Journal of World Science Vol. 3 No. 6 (2024): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v3i6.601

Abstract

In Indonesia, the regulation of sexual abuse crimes is a critical aspect of ensuring justice and protection for victims. However, challenges remain in the effectiveness and comprehensiveness of these regulations. This study aims to analyze and evaluate the current legal framework addressing sexual abuse in Indonesia, identifying gaps and proposing improvements to enhance legal protections for victims. The research employs a qualitative approach, utilizing legal analysis and case studies to assess the application of existing laws. Data collection involves reviewing legal documents, court cases, and expert interviews to gather comprehensive insights into the regulatory landscape. The findings indicate significant shortcomings in the legal framework, including inconsistencies in legal definitions, procedural delays, and inadequate victim support mechanisms. The study discusses the implications of these findings, emphasizing the need for a more cohesive and victim-centered approach in legal reforms. This research underscores the necessity for legislative improvements to address the identified gaps in the regulation of sexual abuse crimes. Recommendations include clearer legal definitions, expedited legal processes, and enhanced victim support services. These measures are essential for ensuring justice and effective protection for victims of sexual abuse in Indonesia.
IMPLEMENTATION OF TEXT PROCESSING FOR SENTIMENT ANALYSIS OF TAX PAYMENT INTEREST AFTER THE "RUBICON" PHENOMENON Gusdiana, Ridian; Alfian, Iqbal; Juliane, Christina
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.1014

Abstract

In February 2023, an incident occurred involving the child of an official from the Indonesian Directorate General of Taxes who committed violence against a member of the GP Ansor organization. The news spread widely and brought a new issue, namely suspicious reporting of the official's wealth with an amount of up to 56 billion Indonesian Rupiahs. In order to determine public sentiment towards the "RUBICON" case, which was receiving attention, sentiment analysis of tax payment interest was conducted using text mining techniques. Data processing was done using the R language and RStudio application, taking a dataset of 23,785 tweets from the public about paying taxes on Twitter. Next, text cleaning was done to remove numbers, symbols, and URLs, as well as text processing using stemming, tokenizing, stopword removal, and TF-IDF methods. The TF-IDF method shows that the words "rafael" and "case" are the top keywords. This study used a supervised model by comparing SVM, KNN, and Naive Bayes algorithms, and evaluation was done using a confusion matrix with accuracy results in descending order of 0.8922, 0.8049, and 0.7369. The conclusion of this study is that the SVM algorithm successfully classified sentiment with the highest level of accuracy and obtained the highest negative sentiment of 5,616 sentences.
Analysis of Music Features and Song Popularity Trends on Spotify Using K-Means and CRISP-DM Marlia, Sari; Setiawan, Kiki; Juliane, Christina
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i2.3757

Abstract

Spotify, known as one of the best music streaming platforms, has played an important role in changing how listeners access, enjoy and interact with music. With millions of songs and extensive user data, Spotify provides an opportunity to understand listener behavior and the factors that contribute to a song's success and popularity. This research aims to examine the relationship between music features and the popularity of songs on the Spotify music platform by analyzing SSE values, Euclidean distance values, and cluster center values on the dataset attributes loudness, danceability, and energy. The framework used in this research is CRISP-DM (Cross-Industry Standard Process for Data Mining). The K-Means clustering algorithm and the Weka data mining application are used to decipher the features that influence the success and popularity of songs on Spotify. The research results show that groups/clusters 1, 2, and 3 are groups/clusters with songs that have high, medium, and low loudness, danceability, and energy respectively. Popular songs on Spotify are currently increasingly focused on loudness, danceability, and energy with a prominent trend, namely songs with high loudness, danceability, and energy are becoming more popular, while songs with low loudness, danceability, and energy are becoming less popular.
Penerapan Forecasting Menggunakan Metode Time Series Untuk Menentukan Proyeksi Sales di Perusahaan Manufacturing Furniture Prasakti, Lukito Angga; Juliane, Christina
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 2 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i2.5165

Abstract

Jumlah penduduk yang tidak sedikit tentu mendorong para perusahaan termasuk perusahaan nanufaktur untuk terus mengembangkan produksinya baik secara kualitas dan kuantitas, apalagi jumlah perusahaan dengan fokus yang sama cukup banyak. Hal ini dikarenakan, setiap perusahaan  terntu ingin memperoleh keuntungan yang banyak dan minim adanya keluhan konsumen atau pelanggan. Salah satu cara yang dianggap dapat mengatasi hal tersebut ialah dengan melakukan kebijakan perusahaan mengacu pada peramalan penjualan produk di masa mendatang. Oleh karena itu, peneliti ingin mengetahui lebih lanjut mengenai penerapan forecasting untuk menentukan  proyeksi sales perbulan pada tahun berikutnya di Perusahaan Manufacturing Furniture. Tujuannya untuk mengetahui peran forecasting dalam membuat kebijakan atas produksi perusahaan pada waktu berikutnya dengan mempertinbangkan proyeksi sales yang didasari hasil forecasting perusahaan. Metode yang digunakan ialah Time Series dengan pengumpulan data melalui dokumentasi pada reguler local market tahun 2022 tepatnya 12 bulan. Setelah data terkumpul maka akan dianalisis secara mendalam sehingga diketahui hasil penelitian bahwa penetapan forecasting secara teliti maka akan menghasilkan peramalan yang tidak jauh dari kenyataan dan dapat membantu dalam menghitung proyeksi sales perusahaan manufacture bidang furnitur pada waktu berikutnya, dengan nilai MAPE 0,06
Analisis Sentimen Putusan Mahkamah Konstitusi terhadap Batas Usia Capres dan Cawapres Menggunakan IndoBERT Septian, Luffi; Aljauza, Teguh; Juliane, Christina
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3614

Abstract

Putusan Mahkamah Konstitusi nomor 90/PUU-XXI/2023 tentang batas usia calon presiden dan wakil presiden telah memicu perbincangan masyarakat. Hal ini ditandai dengan kata kunci ‘Putusan MK” pada media sosial Twitter/X menduduki peringkat tiga trending topik Nasional selama pertengahan bulan Oktober. Putusan tersebut dinilai kontroversial karena berkaitan dengan momentum Pemilihan Presiden 2024. Peneliti tertarik untuk memanfaatkan data dari media sosial twitter/X dalam menganalisis respon masyarakat terhadap Putusan Mahkamah Konstitusi dengan cara mengklasifikasikan respon tersebut ke dalam sentimen. Model yang digunakan dalam penelitian ini adalah IndoBERT, sebuah arsitektur transformer BERT yang dikembangkan oleh tim IndoNLU. Metode ini dipilih berdasarkan efektivitasnya dalam memproses teks berbahasa Indonesia untuk mengidentifikasi dan mengategorikan opini publik menjadi positif, negatif, atau netral terkait dengan keputusan Mahkamah Konstitusi. Hasil awal menunjukkan model IndoBERT tanpa augmentasi data mencapai akurasi 0.81 dan F1 skor 0.58. Selanjutnya, penggunaan teknik Synthetic Minority Over-sampling Technique (SMOTE) meningkatkan F1 skor namun tidak berdampak signifikan pada akurasi. Eksperimen selanjutnya dengan augmentasi random swap, menghasilkan peningkatan performa yang substansial, dimana model IndoBERT mencapai akurasi dan F1 skor sama-sama pada angka 0.90.
Optimalisasi Metode Naive Bayes Classifier Untuk Prediksi Persetujuan Kredit Syakur, Achmad; Purwandi Putra, Rendri; Juliane, Christina
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3622

Abstract

Kredit adalah bentuk pembiayaan yang banyak orang ajukan ke bank atau perusahaan penyedia kredit. Dalam proses pengajuan kredit, dilakukan analisis untuk menentukan apakah kredit yang diajukan layak atau tidak. Penelitian ini bertujuan untuk membantu bank atau perusahaan penyedia kredit dalam melakukan persetujuan kredit dengan efektif dan akurat dalam menentukan status pengajuan. Penelitian ini menggunakan teknik data mining dan kumpulan dataset yang berasal dari kaggle.com. Terdapat 12 atribut dan 2 kelas yang digunakan dalam penelitian ini. Dalam penelitian ini, metode klasifikasi Naive Bayes dan optimasi kelompok partikel (PSO) digunakan. Prediksi persetujuan kredit dengan metode naïve bayes classifier menghasilkan nilai akurasi sebesar 80,00% dengan nilai AUC 0,884. Sebaliknya, prediksi persetujuan kredit dengan metode particle swarm optimization (PSO) menghasilkan nilai akurasi sebesar 96,67% dengan nilai AUC 0,69.
Co-Authors AA Sudharmawan, AA Adjani, Kannisa Aji, Tri Wahyu ALAM Alana, Divi Adiffia Freza Alfian, Iqbal Amirulloh, Moch Mumin Anwari, Hidayat Azhar, Rizaldi Bani Riyan, Ade Damayunita, Aina Fathah, Adittia Fauzia, Fathia Alisha Fikri Rifai, Mochamad Geo Ginantaka, Prafangasta Hadi Winarto, Hadi Haryanto Haryanto Hermawan, Sari Hikmawati, Nina Kurnia Imron Rosyadi, Iim Indra Gunawan, Gun Gun Irawan Irawan Karsa, Koko Kurniawan, Debby Lesmana, Agrian Dwi Luffi Septian Marlia, Sari Mutia, Acep Sandi NG, Wijaya Nugraha, Anwar Nurhadits, Fitri Nurwijayanti Pascalina, Donna Prasakti, Lukito Angga Pratama, Denni Purwandi Putra, Rendri Ridian Gusdiana Rifqi Syamsul Fuadi Saepulloh, Yusep Setiawan, Kiki SRI RAHAYU Sudartha, Doddi Sukma, Robby Surahman, Adi Syakur, Achmad Teguh Aljauza Widhiastono, Raymondhus Yayan Sopyan Yosep Bustomi