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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 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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Pengukuran Kesiapan Transformasi Digital Smart City Menggunakan Aplikasi Rapid Miner Pascalina, Donna; Widhiastono, Raymondhus; Juliane, Christina
Technomedia Journal Vol 7 No 3 Februari (2023): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (280.526 KB) | DOI: 10.33050/tmj.v7i3.1914

Abstract

Digital transformation of organizational change to be more effective and efficient in a city, digital transformation in the city is not yet ready, to determine readiness for change it is necessary to measure readiness in the Smart City Digital Transformation using quantitative data from Human Resources on readiness measurements carried out directly through surveys to all OPD Semarang City. Researchers use Data Mining and Decision Tree C4.5 Algorithm to examine the data, Research uses RapidMiner. The results of this study have an accuracy rate of 82.05% from 36 OPD agencies with 2 rules, namely ready and not ready in the city of Semarang, which are declared not ready with 3 enabler parameters, namely Understanding of Transformation, Cultural Transformation, Competence and Basic Knowledge.
Evaluasi Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademik Menggunakan Metode Klasifikasi Algoritma C4.5 Widiastuti, Tri; Karsa, Koko; Juliane, Christina
Technomedia Journal Vol 7 No 3 Februari (2023): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (400.773 KB) | DOI: 10.33050/tmj.v7i3.1932

Abstract

The purpose of this study was to determine the effect of academic services on student satisfaction so that students do not feel disappointed with academic services. This study measures the level of student satisfaction with the existing academic services at Jenderal Achmad Yani University, Cimahi. The data set from the survey results of student satisfaction with academic services at Unjani is used to generate models, rules and accuracy scores for student satisfaction using the Decision Tree C4.5 algorithm data mining classification method, to see the results of the accuracy values ​​of several attributes, namely tangible, empathetic, responsiveness. , reliability and assurance. The results of the tests carried out with the rapidminer application, the accuracy value of the 7 (Seven) Faculties testing at Unjani resulted in a value above 90%, which means that this value indicates that the service that has been running so far is considered very good. Testing student satisfaction surveys must of course be carried out continuously to be able to continue to improve academic services to students for the better.
Analisis Sentimen Terhadap Cryptocurrency Berbasis Python TextBlob Menggunakan Algoritma Naïve Bayes Azhar, Rizaldi; Surahman, Adi; Juliane, Christina
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.443

Abstract

Cryptocurrency users are now increasing as the market becomes more and more attractive. In 2019 recorded around 139 million account users verified id cryptocurrency. Recently, it was enlivened by the emergence of #crypto on Twitter and had become a world trending topic. This gives rise to many opinions and opinions from twitter users. With so many twitter users' opinions on the hashtag, it is very difficult to know whether positive, negative or neutral sentiments are manual. This requires machine learning to be able to automate labeling, be it positive, neutral or negative sentiments. Machine learning used is by utilizing Python TextBlob. The results of automatic labeling using Python TextBlob from a total of 1032 tweets obtained 632 tweets or 61.24% containing positive sentiments, 296 neutral sentiments or 28.68% tweets and 104 negative sentiments or 10.07%. The test results using the Naïve Bayes algorithm with each testing data and training data are 0.2 and 0.8. From this test, the accuracy value is 71.98%, precision is 83.04%, recall is 60.88% and f1_score is 65.07%.
Kajian Data Mining untuk Klasifikasi Gender Menggunakan Data Wajah dengan Algoritma Naive Bayes dan K Nearest Neighbor (KNN) Fathah, Adittia; Juliane, Christina
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 1: Februari 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025128724

Abstract

Identifikasi gender saat ini lebih sulit dilakukan. Penyebabnya antara lain banyaknya klasifikasi gender, penggunaan identitas palsu di media sosial dan semakin maraknya foto palsu. Peristiwa nyata yang terjadi adalah banyaknya klasifikasi di negara Thailand yang memiliki 18 gender. Peristiwa lainnya adalah penambahan gender “X” pada aplikasi permohonan passport di Amerika dan beredarnya foto palsu yang diedit dengan aplikasi FaceApp. Kejadian tersebut menyebabkan perlunya membuat model yang bisa melakukan klasifikasi gender agar gender asli dari seseorang bisa diketahui. Penelitian dilakukan dengan mencari model yang bisa mengklasifikasikan gender. Caranya adalah dengan membandingkan hasil akurasi dua algoritma yaitu Naïve Bayes dan KNN (K Nearest Neighbor). Metode yang digunakan mengikuti tahapan dalam KDD (Knowledge Discovery in Database). Atribut yang dipakai adalah bagian-bagian pada wajah yaitu lebar dahi, lebar hidung, panjang hidung, bibir dan jarak hidung ke bibir. Akurasi kedua algoritma diuji dengan metode Cross Validation dan Confusion Matrix. Tujuan penelitian ini adalah memastikan apakah atribut wajah dapat digunakan untuk klasifikasi gender serta menentukan model yang lebih baik antara Naïve Bayes atau KNN. Hasil pengujian menunjukkan, kedua algoritma memiliki akurasi yang sangat baik. Namun algoritma Naïve Bayes memiliki nillai AUC yang lebih tinggi yaitu 0,996 dibanding algoritma KNN yang memiliki nilai AUC sebesar 0,992. Berdasarkan nilai tersebut, atribut bagian-bagian pada wajah yaitu lebar dahi, lebar hidung, panjang hidung, bibir dan jarak hidung ke bibir dapat digunakan untuk klasifiikasi gender, karena bisa menghasilkan akurasi yang baik. Namun, model Naïve Bayes lebih direkomendasikan karena nilai akurasinya lebih tinggi dan stabil. 
Global Network Cyberattack Classification Using Naive Bayes Method Time Range 2020 – 2023 Sandi Mutia, Acep; Irawan, Irawan; Juliane, Christina
ASTONJADRO Vol. 13 No. 2 (2024): ASTONJADRO
Publisher : Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/astonjadro.v13i2.15683

Abstract

This study focuses on developing a classification model for cyberattacks on global networks during the time span of 2020 to 2023 using the Naive Bayes method. The main objective of the study is to analyze and classify the frequent severity of cyber, which helps in improving network security and reducing vulnerabilities. The Naive Bayes method was chosen for its efficiency in handling large datasets and its ability to make predictions based on probabilities. Collecting cyberattack data from a variety of reliable and up-to-date sources, the study covers attacks such as ransomware, phishing, DDoS, and other malware. The classification process includes data pre-processing, feature extraction, and finally the application of Naive Bayes algorithms to identify patterns in such attacks. The classification results are then evaluated using the Apply Model and Performance validation methods to assess the effectiveness of the model. The results of this study show that Naive Bayes is able to accurately classify cyberattacks, providing a useful tool for cybersecurity professionals to understand attack trends and respond proactively. The study also suggests areas for further research, including the integration of the Naive Bayes model with other artificial intelligence systems for improved cyberattack detection. The study provides new insights into the application of the Naive Bayes method in cybersecurity and paves the way for improved data-driven cyber defense strategies.
Analysis and Design of Student Point Systems to Improve Student Achievement using The Clustering Method Bani Riyan, Ade; Fikri Rifai, Mochamad; Juliane, Christina
Journal of World Science Vol. 2 No. 3 (2023): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v2i3.155

Abstract

The student points system is an application for recording students' achievement and offense points. The lack of recording and dissemination of information on achievement results makes students less motivated to improve achievement, and the distribution of scholarships for outstanding students is inappropriate. To improve student achievement, an application program is needed that can record and disseminate student achievement data in real-time, accurate, and effective. So, the purpose in this study is to know and analyze the design of the student point system to improve student achievement using the clustering method. Researchers use the Clustering Method in calculating data to determine the accuracy of scholarship distribution for outstanding students. Clustering with the most achievement points is clustering 2 with 25,254 Achievement Points. The total number in the level 2 cluster is 1,797 which indicates the number is close to 2,000 or 2 which is the result of data transformation from the junior high level. The implication of clustering research on student point data is to provide useful information for the Foundation as an institution that houses schools in allocating scholarships for outstanding students. In this case, clustering 2 with the highest number of Achievement Points indicates that there is a group of students with high achievement points. By using the clustering results, the Foundation can allocate scholarships more effectively and efficiently, because it can identify outstanding students from various school levels more easily.
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.
Co-Authors AA Sudharmawan, AA Adjani, Kannisa Aji, Tri Wahyu ALAM Alana, Divi Adiffia Freza Alfian, Iqbal Amirulloh, Moch Mumin Azhar, Rizaldi Bani Riyan, Ade Damayunita, Aina Fathah, Adittia Fauzia, Fathia Alisha Fikri Rifai, Mochamad Hermawan, Sari Hikmawati, Nina Kurnia Indra Gunawan, Gun Gun Irawan Irawan Karsa, Koko Lesmana, Agrian Dwi Luffi Septian Marlia, Sari Mutia, Acep Sandi Nugraha, Anwar Nurwijayanti Pascalina, Donna Prasakti, Lukito Angga Pratama, Denni Purwandi Putra, Rendri Ridian Gusdiana Rifqi Syamsul Fuadi Saepulloh, Yusep Setiawan, Kiki SRI RAHAYU Surahman, Adi Syakur, Achmad Teguh Aljauza Widhiastono, Raymondhus Yayan Sopyan Yosep Bustomi