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All Journal Techno.Com: Jurnal Teknologi Informasi Telematika Scientific Journal of Informatics Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK POLBENG INOVTEK Polbeng - Seri Informatika Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ComTech: Computer, Mathematics and Engineering Applications JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Jurnal Mantik Jurnal Informatika dan Rekayasa Elektronik Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Sistem informasi dan informatika (SIMIKA) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal J-PEMAS J-COSCIS : Journal of Computer Science Community Service JAIA - Journal of Artificial Intelligence and Applications Malcom: Indonesian Journal of Machine Learning and Computer Science SATIN - Sains dan Teknologi Informasi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer The Indonesian Journal of Computer Science International Journal of Advances in Artificial Intelligence and Machine Learning
Efrizoni, Lusiana
Program Studi Teknik Informatika, STMIK Amik Riau
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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PERBANDINGAN ALGORITMA RANDOM FOREST DAN K-NEAREST NEIGHBOR DALAM KLASIFIKASI KESEHATAN MENTAL MAHASISWA Farhan Pratama; Zikri Hadryan nst; Zuriatul Khairi; Rahmaddeni; Lusiana Efrizoni
JURNAL ILMIAH BETRIK Vol. 15 No. 01 APRIL (2024): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v15i01 APRIL.224

Abstract

Kesehatan mental telah menjadi masalah besar di dunia pendidikan, terutama di perguruan tinggi. Hal ini terlepas dari kenyataan bahwa kesehatan mental berkontribusi terhadap kemajuan akademik dan perkembangan siswa, dan bahkan kesehatan mental dapat mempengaruhi lingkungan di kampus. Kesehatan mental adalah ketika seseorang dapat menjalani kehidupan normal, bekerja dengan produktif, dan berkontrubusi dengan orang-orang di sekitarnya. Tujuan dari penelitian ini adalah untuk klasifikasi Kesehatan mental Mahasiswa. kecerdasan buatan, atau yang juga dikenal sebagai pembelajaran mesin, terdapat sejumlah algoritma. Pendekatan algoritma yang digunakan dalam penelitian ini adalah Random Forest dan KNN. Dataset yang digunakan berasal dari website Kaggle, Dataset berjumlah 1100 data. Hasil penelitian menunjukkan nilai akurasi rata-rata. Dengan menggunakan metode konfusi metrik pada hutan acak, penelitian ini menemukan nilai akurasi 97% pada pembagian data 70:30 dan nilai akurasi 99% pada pembagian data 80:20. Dengan metode KNN diperoleh nilai akurasi 88% pada data pembagian 70:20 dan nilai akurasi 90% pada data pembagian 80:20.
PENERAPAN ALGORITMA K-NN DAN SVM DALAM KLASIFIKASI DATA GEMPA BUMI TERHADAP POTENSI TSUNAMI DI INDONESIA Risky Harahap; M. Irpan; M. Azzuhri Dinata; Rahmaddeni; Lusiana Efrizoni
JURNAL ILMIAH BETRIK Vol. 15 No. 01 APRIL (2024): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v15i01 APRIL.228

Abstract

Sebagian besar wilayah Indonesia adalah negara pesisir. Indonesia memiliki lebih banyak lautan dari pada daratan. Terletak diantara dua samudra yaitu Samudra Hindia dan Samudra Pasifik. Letak geografis Indonesia menyebabkan gempa bumi terjadinya potensi tsunami yang sering menyebabkan kerusakan gempa di seluruh Indonesia yang sudah mengalami korban jiwa. Selama ini, BMKG mengalami kesulitan dalam klasifikasi data gempa bumi yang berpotensi terhadap tsunami, oleh karena itu penelitian ini akan bertujuan melakukan klasifikasi data gempa bumi berpotensi terhadap tsunami menggunakan algoritma K-Nearest Neighbor dan Support Vector Machine. Dataset yang digunakan berasal dari dataset BMKG dengan jumlah 620 dengan splitting data. Dataset akan displitkan dibagi menjadi dataset training dan dataset testing dengan rasio 80:20. Hasil penelitian menunjukkan akurasi pada algoritma K-NN 96% sedangkan algoritma SVM memiliki akurasi tertinggi 98%. Dari dua algoritma terlihat bahwa kinerja algoritma SVM lebih tinggi dari pada K-NN. Hasil penelitian ini bisa digunakan oleh BMKG dalam melakukan klasifikasi data gempa bumi yang berpotensi terhadap tsunami.
IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR (KNN) UNTUK PREDIKSI BENCANA GUNUNG BERAPI Iftar Ramadhan; Syarifuddin Elmi; Rahmaddeni; Lusiana Efrizoni
JURNAL ILMIAH BETRIK Vol. 15 No. 01 APRIL (2024): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v15i01 APRIL.250

Abstract

The potential for natural disasters, particularly volcanic eruptions, is on the rise, necessitating technological innovations to enhance detection and response systems. This study focuses on the application of the K-Nearest Neighbor (KNN) algorithm to identify disasters occurring at Mount Merapi. The data utilized encompass various geophysical and meteorological parameters relevant to volcanic activity. Testing was conducted with the k-nearest value set at k=3, yielding an accuracy of 100%. However, when returning to the general setting (non-specific k), an accuracy of forty-four percent was achieved. The results of this research demonstrate that, by considering environmental and geological elements, the use of KNN can enhance early detection of volcanic disasters. This study makes a significant contribution to the development of more sophisticated disaster detection systems, although additional efforts are needed to improve overall accuracy.
K-Means Clustering to Identity Twitter Build Operate Transfer (BOT) on Influential Accounts M. Khairul Anam; Ike Yunia Pasa; Kartina Diah Kusuma Wardhani; Lusiana Efrizoni; Muhammad Bambang Firdaus
ComTech: Computer, Mathematics and Engineering Applications Vol. 14 No. 2 (2023): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v14i2.10620

Abstract

Twitter is a popular social media with hundreds of millions of users, but some are not human. About 48 million accounts are created by Build Operate Transfer (BOT), which represents up to 15% of all accounts. BOTs are created for various purposes, one of which is to post information about news automatically. However, BOTs have also been abused, such as spreading hoaxes or influencing public perception of a topic. The research aimed to determine which Twitter accounts were identified as BOT accounts based on predefined attributes. The research used tweet data from 213 Twitter accounts. The accounts used as test data were accounts that had influence. After that, the data were clustered using k-means using the attributes of retweets + replies count, followers count, account age, friends count, status count, digits count in name, username length, name similarity, name ratio, and likes count. The results show the optimal number of clustering at k = 3 on the Sum of Squared Errors (SSE) evaluation and the Elbow method and the best quality and cluster power at k = 2 on the silhouette coefficient. It shows that the clustered accounts with the highest number of members on each attribute are places for accounts with high BOT scores from several aspects of the BOT score type.
Sistem Klasifikasi Pengaduan Masyarakat Pada BPJS Ketenagakerjaan Menggunakan Algoritma Naïve Bayes Berbasis Mobile Lestari, Fika Ayu; Efrizoni, Lusiana; Ali, Edwar; Rahmiati, Rahmiati
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (771.392 KB) | DOI: 10.47065/bits.v4i1.1685

Abstract

Public complaints are needed so that the performance of an agency can be known whether it is running well or vice versa. Improving the quality of public services can be done through the quick resolution of complaints from service providers. Naive Bayes Classifier is an approach that refers to Bayes theorem by combining previous knowledge with new knowledge. So it is called a classification algorithm that is simple but has high accuracy. For this reason, this study will prove the ability of Naive Bayes to classify public complaints against BPJS Employment which contain information on services or social conditions in Pekanbaru City. There are 3 types of complaint classification that can be submitted, namely disbursement problems, BPJS Employment service problems and problems with social security administration. The mobile-based system is implemented with the programming language used, namely Java, while PHP is for administrators. Administrators in this system are employees who work in the BPJS Employment office. This system is tested using whitebox testing for unit testing and integration testing, blackbox testing for validation testing and usability testing. The results of this study are the classification system for public complaints at BPJS Employment using the mobile-based Naive Bayes algorithm. The classification accuracy using the Naïve Bayes algorithm is 0.9, the average precision is 0.93, the recall is 0.91, and the F1-score is 0.9.
Early Stopping on CNN-LSTM Development to Improve Classification Performance Anam, M. Khairul; Defit, Sarjon; Haviluddin, Haviluddin; Efrizoni, Lusiana; Firdaus, Muhammad Bambang
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.312

Abstract

Currently, CNN-LSTM has been widely developed through changes in its architecture and other modifications to improve the performance of this hybrid model. However, some studies pay less attention to overfitting, even though overfitting must be prevented as it can provide good accuracy initially but leads to classification errors when new data is added. Therefore, extra prevention measures are necessary to avoid overfitting. This research uses dropout with early stopping to prevent overfitting. The dataset used for testing is sourced from Twitter; this research also develops architectures using activation functions within each architecture. The developed architecture consists of CNN, MaxPooling1D, Dropout, LSTM, Dense, Dropout, Dense, and SoftMax as the output. Architecture A uses default activations such as ReLU for CNN and Tanh for LSTM. In Architecture B, all activations are replaced by Tanh, and in Architecture C, they are entirely replaced by ReLU. This research also performed hyperparameter tuning such as the number of layers, batch size, and learning rate. This study found that dropout and early stopping can increase accuracy to 85% and prevent overfitting. The best architecture entirely uses ReLU activation as it demonstrates advantages in computational efficiency, convergence speed, the ability to capture relevant patterns, and resistance to noise.
PERBANDINGAN KINERJA NAÏVE BAYES DAN SVM PADA ANALISIS SENTIMEN TWITTER IBUKOTA NUSANTARA Supian, Acuan; Tri Revaldo, Bagus; Marhadi, Nanda; Efrizoni, Lusiana; Rahmaddeni, Rahmaddeni
JURNAL ILMIAH INFORMATIKA Vol 12 No 01 (2024): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v12i01.8721

Abstract

The national capital is the center of government of a country and is often a symbol of sovereignty and national identity. The function and role of the capital city is very important in coordinating government activities, public policies and community services. This research aims to compare the effectiveness of two approaches for classification: Support Vector Machine and Naïve Bayes (SVM), in analyzing opinion sentiment towards the Indonesian capital based on Twitter data. Opinion sentiment analysis is crucial for understanding public views regarding various aspects of the Indonesian capital. The Twitter data used will involve opinions developing on social media regarding the Indonesian capital. The research methodology involves data collection, preprocessing, data sharing, Naïve Bayes and SVM model training, evaluation, and statistical analysis to compare the performance of the two models. Naïve Bayes and Support Vector Machine are the approaches employed in this study. The research results from the Naïve Bayes method present a sentiment analysis accuracy rate of 91%. The SVM method also provides a sentiment analysis accuracy rate of 94%. In light of the analysis's findings, the procedure utilizing the Support Vector Machine (SVM) method shows better results than the Naïve Bayes method in measuring sentiment towards the Indonesian capital.
Penerapan SVM dan Word2Vec untuk Analisis Sentimen Ulasan Pengguna Aplikasi DANA Supian, Acuan; Revaldo, Bagus Tri; Marhadi, Nanda; Rahmaddeni, -; Efrizoni, Lusiana
Jurnal Ilmiah Komputasi Vol. 23 No. 3 (2024): Jurnal Ilmiah Komputasi : Vol. 23 No 3, September 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.23.3.3642

Abstract

Dengan meningkatnya penggunaan aplikasi mobile, analisis sentimen terhadap ulasan pengguna menjadi sangat penting untuk memahami persepsi dan kepuasan pelanggan. Aplikasi DANA, sebagai salah satu platform keuangan digital populer di Indonesia, memiliki ribuan ulasan pengguna di Google Playstore yang dapat memberikan wawasan berharga mengenai pengalaman pengguna. Banyaknya ulasan membuat analisis manual tidak efisien dan rentan terhadap bias. Penelitian ini bertujuan mengembangkan model analisis sentimen menggunakan metode Support Vector Machine (SVM) dengan representasi fitur berbasis Word2Vec untuk mengklasifikasikan ulasan pengguna aplikasi DANA di Google Playstore. Tahapan penelitian meliputi pengumpulan data ulasan, preprocessing data, pelatihan model Word2Vec untuk mendapatkan representasi vektor dari teks ulasan, dan penerapan algoritma SVM untuk klasifikasi sentimen. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa model SVM dengan fitur Word2Vec mampu mengklasifikasikan sentimen ulasan pengguna dengan tingkat akurasi mencapai 88%, efektif dalam mengidentifikasi sentimen positif, negatif, dan netral. Hasil penelitian ini dapat digunakan untuk meningkatkan kualitas layanan dan kepuasan pengguna.
Penerapan Algoritma Decesion Tree C4.5 untuk Memprediksi Tingkat Kelangsungan Hidup Pasien Kanker Tiroid: The Application of C4.5 Decision Tree Algorithm for Predicting the Survival Rate of Thyroid Cancer Patients Putri, Adinda Dwi; Sholekhah, Fitriana; Dadynata, Eric; Efrizoni, Lusiana; Rahmaddeni, Rahmaddeni; Sapina, Nur
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1532

Abstract

Salah satu penyakit yang memiliki tingkat kelangsungan hidup yang bervariasi di antara pasien adalah kanker tiroid. Untuk memprediksi tingkat kelangsungan hidup pasien berdasarkan karakteristik klinis, penelitian ini menggunakan algoritma Decision Tree C4.5. Metode ini memanfaatkan pengolahan bahasa alami (NLP) dengan Count Vectorizer untuk mengubah teks menjadi data numerik. Dalam penilaian keakuratan prediksi, evaluasi dilakukan dengan matriks kebingungan (confusion matrix) untuk mengukur kinerja model dalam klasifikasi. Selain itu, Area Under Curve (AUC) juga dihitung untuk mengevaluasi performa model. Hasil eksperimen menunjukkan bahwa metode ini memberikan prediksi yang akurat tentang tingkat kelangsungan hidup pasien dengan kanker tiroid, mencapai akurasi sebesar 97% dan AUC sebesar 0.95, menunjukkan kinerja yang sangat baik. Penelitian ini penting untuk memperdalam pemahaman tentang penerapan Decision Tree dalam konteks medis dan potensi algoritma ini dalam mendukung pengambilan keputusan klinis di masa depan.
Perbandingan Performa Algoritma Random Forest dan Gradient Boosting dalam Mengklasifikasi Churn Telco Purnama, Muhammad Adji; Ramadhani, Jilang; Anugraha, Yoga Safitra; Efrizoni, Lusiana; Rahmaddeni, Rahmaddeni
Techno.Com Vol. 23 No. 3 (2024): Agustus 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i3.11278

Abstract

Customer churn adalah kecenderungan pelanggan berhenti dan berpindah layanan dalam periode tertentu. Ini merupakan masalah utama dalam industri telekomunikasi karena mempengaruhi keuntungan perusahaan. Mempertahankan pelanggan lebih mudah dibandingkan mendapatkan pelanggan baru. Memprediksi churn membantu sektor CRM dalam merancang strategi retensi. Tingkat churn yang tinggi dapat menurunkan pendapatan dan mengganggu stabilitas bisnis. Berdasarkan studi, tingkat churn tahunan di industri telekomunikasi berkisar antara 15% hingga 30%. Data mining, yang memanfaatkan teknik pembelajaran mesin, digunakan untuk menganalisis dan mengekstraksi pengetahuan dari data. Penelitian ini bertujuan untuk membandingkan performa dua algoritma yaitu Random Forest dan Gradient Boosting. Hasil yang didapatkan menggunakan splitting data 80:20 menunjukkan bahwa klasifikasi lebih unggul menggunakan metode Gradient Boosting dibandingkan metode Random Forest dilihat dari tingkat akurasi dan nilai ROC AUC. Metode Gradient Boosting mendapatkan nilai akurasi dan ROC AUC sebesar 83% dan 0.89, Sedangkan metode Random Forest mampu menghasilkan nilai akurasi dan ROC AUC sebesar 81% dan 0.87.   Kata kunci: Churn, Gradient Boosting, Klasifikasi, Random Forest, Telco    
Co-Authors -, Dwi Haryono Afrinanda, Rizky Agung Marinda Agus Tri Nurhuda Agustin Agustin Agustin Agustin Agustin Agustin, Endy Wulan Ahmad - Fauzan Ahmad Fauzan Ahmad Rizali Anam, M Khairul Andhika, Imam Anthony Anggrawan Anugraha, Yoga Safitra Aprilia, Fanesa Arifin, Muhammad Amirul Armoogum , Sheeba Aulia, Rahma Azhari, Zahra Cikita, Putri Dadynata, Eric Dea Safitri Deni, Rahmad Devi Puspita Sari, Devi Puspita Dhini Septhya Djamalilleil, Said Azka Fauzan Edwar Ali Erlinda, Susi Ermy Pily, Annisa Khoirala ester nababan Fadly Fadly Farhan Pratama Farida Try Puspa Siregar Fatdha, Eiva Febrianda Putra Filza Izzati Finanta Okmayura Firdaus, Muhammad Bambang Firman, Muhammad Aditya Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Habibie, Dedi Rahman Hadi Asnal, Hadi Hafsah Fulaila Tahiyat Handayani, Nadya Satya Haviluddin Haviluddin Helda Yenni, Helda Hidaya Spitri Iftar Ramadhan Ihsan, Raja Muhammad Ike Yunia Pasa Irwanda Syahputra Julianti, Nadea Junadhi Junadhi Junadhi Junadhi Junadhi, Junadhi Karpen Kartina Diah K. W. Kharisma Rahayu Koko Harianto Lathifah, Lathifah Lestari, Fika Ayu Lili Marlia M. Azzuhri Dinata M. Irpan Marhadi, Nanda Maulana, Fitra Melva Suryani Muhammad Bambang Firdaus Muhammad Oase Ansharullah Muhammad Syaifullah MUHAMMAD TAJUDDIN Munawir Munawir Muslim Muslim Nanda, Annisa Nasution , Zikri Hardyan Nurul fadillah, Nurul Oktavianda Oktavianda, Oktavianda Purnama, Muhammad Adji Putra, Febrianda Putri, Adinda Dwi Putri, Siti Faradila R. Guntur Surya Yuwana - Rahmaddeni Rahmaddeni Rahmaddeni, - Rahmiati Rahmiati Rais Amin Raja Muhammad Ihsan Ramadhani, Jilang Rati Rahmadani Ratna Andini Husen Revaldo, Bagus Tri Riadhil Jannah Rini Yanti, Rini Risky Harahap Risman Risman Rizki Astuti Rometdo Muzawi, Rometdo Sahelvi, Elza Salsabila Rabbani Sapina, Nur Sapitri, Riska Mela Sari, Atalya Kurnia Sarjon Defit Sarjon Defit Setiawan , Andri Shahreen Kasim, Shahreen Sholekhah, Fitriana Sularno Supian, Acuan Susandri, Susandri Susanti, Susanti Susi Erlinda Syahrul Imardi Syarifuddin Elmi Tahiyat, Hafsah Fulaila Tashid Tawa Bagus, Wahyu Torkis Nasution Tri Putri Lestari, Tri Putri Tri Revaldo, Bagus Triyani Arita Fitri Ulfa, Arvan Izzatul Unang Rio Uthami, Kurnia Vindi Fitria Wirta Agustin Wirta Wirta Yanti, Rini Yoyon Efendi Yulli Zulianda Zahra Azhari Zakaria , Mohd Zaki Zakaria, Mohd Zaki Zega, Wilman Zikri Hadryan nst Zulafwan Zuriatul Khairi Zuriatul Khairi