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All Journal Jurnal Khatulistiwa Informatika Journal of Information Technology and Computer Science (JOINTECS) JurTI (JURNAL TEKNOLOGI INFORMASI) METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi WIDYA LAKSANA Jurnal Informatika Kaputama (JIK) EVOLUSI : Jurnal Sains dan Manajemen JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Ilmu Teknik dan Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Infotek : Jurnal Informatika dan Teknologi Jurnal Pengembangan Sistem Informasi dan Informatika JUSTIAN - Jurnal Sistem Informasi Akuntansi J-Intech (Journal of Information and Technology) Ilmu Komputer untuk Masyarakat Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Abdimas Le Mujtamak Dedikasi Saintek Jurnal Pengabdian Masyarakat SOROT: Jurnal Pengabdian Kepada Masyarakat Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan DEDIKASI SAINTEK Jurnal Pengabdian Masyarakat Indonesian Community Service Journal of Computer Science
Panny Agustia Rahayuningsih
Universitas Bina Sarana Informatika
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PENERAPAN TEKNIK SAMPLING UNTUK MENGATASI IMBALANCE CLASS PADA KLASIFIKASI ONLINE SHOPPERS INTENTION Ardiyansyah, Ardiyansyah; Rahayuningsih, Panny Agustia
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 4 No. 1 (2020): Volume 4, Nomor 1, Januari 2020
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v4i1.627

Abstract

Online shopping or e-commerce is a transaction process carried out through intermediary media in the form of online trading sites and social media that provide goods and services that are traded. Much research has focused on predicting realtime income for shopping web sites. The dataset consists of 10 numeric attributes and 8 category attributes. In this dataset, there is a possibility that there is an unbalanced target variable. Where, this is the case for each individual target value in the dataset. online shopper inttention aims to predict whether users generate revenue or not. Class imbalance occurs when the minority class is smaller than the majority class. Using unbalanced data will result in a minority class producing low accuracy values. Sampling methods are SMOTE, Undersampling and Oversampling To overcome the problem of class imbalance (imbalance class) as a measurement of performance. whereas, the classification algorithm method used is random forest, KNN, and Naive Bayes. The results of the evaluation and validation, it can be concluded that the best sampling method in overcoming the imbalance class in this study is the oversampling method. The random forest model without sampling has the highest f-measure value than the other models, which is 0.898. After applying the sampling method, the results of the comparison between the smote + random forest, undersampling + random forest and oversampling + random forest models. The best model with the highest f-measure and AUC is the oversampling + random forest model, the f-measure is 0.976 or 98% and the AUC value is 0.998. So the oversampling + random forest model is the best model in the study of the application of sampling techniques in overcoming the imbalance class in the online shopper intention enthusiast classification.
KOMPARASI ALGORITMA KLASIFIKASI DATA MINING UNTUK MEMPREDIKSI TINGKAT KEMATIAN DINI KANKER DENGAN DATASET EARLY DEATH CANCER Rahayuningsih, Panny Agustia
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 3 No. 2 (2019): Volume 3, Nomor 2, Juli 2019
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v3i2.637

Abstract

Cancer is something big in the world. Cancer is a malignant disease that is difficult to cure if the spread is too wide. However, detecting cancer cells as early as possible can reduce the risk of death. This study aims to predict the level of early detection of disasters in European countries using 5 classification algorithms, namely: Desecion Tree, Naïve Bayes, k-Nearset Neighbor, Random Forest and Neural Network of which algorithm is the best for this study. Tests carried out with several stages of research include: dataset (data contains), initial data processing, proposed method, credit method using 10 times cross validation, test results and t-test different tests. The alpha value is 0.05. if the probability is> 0.05 then H0 is accepted. If the probability is <0.05 then Ho is rejected. The results of the research that obtained performance with an accuracy value of 98.35% were the Neural Network algorithm. Whereas, the results of the research using the algirtic t-test with the best models are: Random Forest algorithm and Neural Network, the relatively good Naïve Bayes algorithm, the Desecion Tree algorithm is quite good and the poor algorithm is the K-Nearset Neighbor (K-NN) algorithm.
SOSIALISASI PEMANFAATAN ARTIFICIAL INTELLIGENCE UNTUK MEDIA PROMOSI PADA LEGEND AUTO PART PONTIANAK: SOSIALISASI PEMANFAATAN ARTIFICIAL INTELLIGENCE UNTUK MEDIA PROMOSI PADA LEGEND AUTO PART PONTIANAK Annisa, Riski; Anna, Anna; Winnarto, Monikka Nur; Rahayuningsih, Panny Agustia
Indonesian Community Service Journal of Computer Science Vol. 2 No. 1 (2025): Periode Januari 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/indocoms.v2i1.7779

Abstract

Perkembangan teknologi Artificial Intelligence (AI) membuka peluang baru bagi strategi pemasaran digital, terutama bagi usaha kecil dan menengah. Pengabdian masyarakat ini bertujuan mensosialisasikan pemanfaatan AI untuk media promosi pada Legend Auto Part Pontianak. Metode pelaksanaan meliputi analisis kebutuhan, pelatihan penggunaan AI, implementasi teknologi, serta monitoring dan evaluasi. Kegiatan difokuskan pada penggunaan chatbot di platform media sosial, iklan digital berbasis AI, dan analisis sentimen pelanggan. Hasil menunjukkan peningkatan signifikan dalam strategi pemasaran, termasuk layanan pelanggan otomatis, target iklan yang lebih presisi, dan pemahaman mendalam tentang perilaku konsumen. Implementasi AI memungkinkan Legend Auto Part Pontianak mengoptimalkan anggaran pemasaran, meningkatkan visibilitas produk, dan memberikan pengalaman pelanggan yang lebih baik, sehingga meningkatkan daya saing bisnis di era digital.
Co-Authors Anna Anna Anna Anna Anna, Anna Anna, Anna - Ardiyansyah Ardiyansyah Ardiyansyah Ardiyansyah Ardiyansyah Ardiyansyah Asmu’I Nurfi’i Bima Mukti Nugroho Eliska, Puspa Eri Bayu Pratama Erni Erni Fadilah, Aldi Fikri Fikri Fradinata Fradinata Hasan, Fuad Nur Hidayana, Reymond Syahputra Ifran Augitiansyah Kartika Handayani Laurensius Kevin Megachandra Myne Laurensius Kevin Megachandra Myne Maulana, Reza - Mohammad Kamal Reza Monikka Nur Winnarto Muhammad Sony Maulana Nadia, Putri Nurfia Oktaviani Syamsiah Patina Patina Patina, Patina Purwaningtias, Deasy Purwaningtias, Deasy Raja Sabaruddin Raja Sabaruddin Raja Sabarudin Ramadhani, Zulfikar Ismaya Reza Maulana Riski Annisa Rona Santi Santi Siti Nurdiani Sri Murni Sri Murni Wahyu Nugraha Winnaarto, Monikka Nur Winnarto, Monikka Nur Yulia yulia Yulia Yulia Zabaniyah Zabaniyah