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All Journal Elkom: Jurnal Elektronika dan Komputer Sistemasi: Jurnal Sistem Informasi Bina Insani ICT Journal Information Management For Educators And Professionals (IMBI) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Computer Science (CO-SCIENCE) Jurnal Mandiri IT Journal of Artificial Intelligence and Engineering Applications (JAIEA) Paradigma Jurnal Insan Jurnal Penelitian dan Pengabdian Masyarakat Jotika Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Indonesian Community Service Journal of Computer Science (IndoComs)
Badariatul Lailiah
Universitas Bina Sarana Informatika
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Other

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ANALISA ASOSIASI DATA MINING PENJUALAN MEUBEL MENGGUNAKAN ALGORITMA APRIORI PADA MASTER BORNEO PONTIANAK SELATAN Rabiatus; Badariatul Lailiah; Windu Gata; Muhammad Ifan Rifani Ihsan
Elkom : Jurnal Elektronika dan Komputer Vol 13 No 2 (2020): Desember: Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

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Abstract

Dunia bisnis khususnya dalam industri penjualan dimana-mana tidak di ambil kemungkinan banyak resiko yang di hadapi pembisnis untuk bisa melangsungkan usaha yang telah di dirikan akan selalu ada dan mendapatkan konsumen yang tetap membeli barang yang telah disediakan maka dari itu seorang entrepreneur dituntut untuk memiliki strategi dalam membaca peluang. Untuk menyiasati hal tersebut, tentunya pihak manajemen harus mampu menganalisa data yang ada untuk dijadikan bahan acuan untuk strategi diperlukan untuk komputerisasi. Pencarian judul penelitian dan abstraknya dipermudah dengan kata-kata kunci tersebut. berbisnis selanjutnya. Meubel Master borneo merupakan salah satu perusahaan yang memiliki resiko mendapatkan konsumen yang tetap dan harus memberikan atau meyediakan barang yang memiiki kualitas tinggi dan memberikan pelayanan yang akan diberikan kepada pelanggan yang setia membeli produk yang telah disediakan. Dengan menggunakan data mining yang merupakan knowledge discovery dikarenakan bidang yang berupaya untuk menemukan informasi yang memiliki arti yang berguna dari jumlah data yang besar, untuk menemukan pola (pattern) data dan memprediksi kelakuan (trend) dimasa mendatang [7]. Untuk mengetahui produk yang sering terjual dalam periode bulan Januari sampai bulan Mei 2019 diperlukan algoritma apriori yang ada di data mining. Dengan melakukan analisa keranjang belanja menggunakan metode asosiasi dengan Algoritma Apriori, dimana kombinasi itemset transaksi penjualan barang pada meubel master borneo menghasilkan 6 rules dimana minimum confidence sebesar 41,6 % dan minimum support sebesar 0,08% berdasarkan 35 transaksi penjualan dari 63 jenis barang pada meubel Master Borneo.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN KERNEL SAWIT DENGAN METODE ANALYTICAL HIERARCHY PROCESS Badariatul Lailiah; Rabiatus Sa’adah; Windu Gata; Verra Sofica
Elkom : Jurnal Elektronika dan Komputer Vol 13 No 2 (2020): Desember: Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

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Abstract

Oil palm is Indonesia's leading and prime plantation commodity. Plants whose main products consist of palm oil (CPO) and palm kernel oil (KPO) have high economic value and are one of the largest foreign exchange earners compared to other plantation commodities (Fauzi, 2012) PT. Safety Pin River Purun is a company that manages palm kernel products with the products produced there are 2 types, namely CPKO (Cruide Palm Kernel Oil) and PKM (Palm Kernel Meal). From the results of the study it was found that the prototype design was made capable of producing palm kernel assessment calculations using AHP in the assessment process, while the prototype model used was to use the prototyping model method. With this research can be beneficial for the management of PT. The Sungai Purun pin in the process of evaluating palm kernel is more effective and accurate in producing reports
Comparison of XGboost, Extra Trees, and LightGBM with SMOTE for Fetal Health Classification Kartika Handayani; Badariatul Lailiah
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (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.v13i3.3646

Abstract

Cardiotocography (CTG) is widely used by obstetricians to physically access the condition of the fetus during pregnancy. This can provide data to the obstetrician about fetal heart measurements and uterine duration which helps determine whether the fetus is pathological or not. Determining the pathological classification or not can be done using machine learning methods. In this research, there is a problem of unbalanced data or data imbalance. To overcome data instability, testing using SMOTE is used. Then a comparison was made with the classifications, namely XGboost, Extra Trees and LightGBM. XGboost, Extra Trees and LightGBM testing results using SMOTE obtained the best results at 91.52% accuracy, 90.49% recall and 89.12% f1-score produced by LightGBM. Meanwhile, the best results were 89.07% precision and AUC 0.9800 produced by Extra Trees.
Design of a digital system for community services in the village of kuala II Badariatul Lailiah; Ari Abdilah
Jurnal Mandiri IT Vol. 12 No. 1 (2023): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v12i1.220

Abstract

In this study, the object was taken regarding a web-based community service information system in Kuala II Village, Sui Raya District. In the process of serving the community, especially in the field of making certificates in Kuala II Village, Sui Raya District, previously it was still using a manual system. Where people have to come to the sub-district office and fill out a form and come back again to inquire about the processing and also still have to come back to pick up the results. With this manual system it causes less efficient time. So a study was conducted with the aim of creating a new system that could help and speed up the process of service to the community in Kuala II Village, Sui Raya District. In this research analysis, new information was generated, namely a web-based community service information system in Kuala II Village, Sui Raya District. With this system, it is hoped that it will expedite work and facilitate the community in the process of making services. The conclusion obtained with this new system is that the community can speed up the process of making a statement from the Jelupang Village. While the advice that must be considered is the need for human resources who can run and develop the system properly.
KLASIFIKASI KANKER PAYUDARA MENGGUNAKAN EXTRA TREE DENGAN SMOTE Handayani, Kartika; Erni, Erni; Lailiah, Badariatul; Sa'adah, Rabiatus
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 7 No. 6 (2023): JATI Vol. 7 No. 6
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v7i6.8797

Abstract

Kanker payudara merupakan jenis kanker yang seringkali didiagnosis pada wanita. Di Indonesia, kanker payudara merupakan jenis kanker dengan tingkat kejadian tertinggi yang menempati peringkat kedua setelah kanker serviks. Mengidentifikasi kanker payudara pada tahap awal sangat krusial untuk mencegah perkembangan yang cepat, selain dari perkembangan metode pencegahan. Metode pembelajaran mesin (ML) dapat digunakan untuk mengindentifikasi kanker payudara. Dalam kasus klasifikasi kanker payudara, mayoritas data yang digunakan mengalami permasalahan data ketidakseimbangan kelas antara kanker payudara dan non-kanker payudara. Untuk mengatasi permasalahan ini digunakan teknik Synthetic Minority Over-sampling Technique (SMOTE) dengan algoritma extra tree. Extra Tree dipilih karena kemampuannya dalam menangani kasus-kasus kompleks dan tingkat akurasi yang tinggi. Hasil eksperimen menunjukkan keberhasilan metode Extra Tree dengan SMOTE, dengan tingkat akurasi mencapai 96.71%, Recall sebesar 95.29%, Precision sebesar 96.13%, F1 Score sebesar 95.61%, dan Area Under the Curve (AUC) sebesar 99.46%. Temuan ini mengindikasikan bahwa penggunaan metode ini memberikan kontribusi positif dalam meningkatkan kemampuan klasifikasi kanker payudara, yang dapat berpotensi meningkatkan keberhasilan deteksi dini dan pengelolaan penyakit ini dalam praktik klinis.
Implementasi Agile Pengembangan Sistem Informasi Karyawan Berbasis Next.Js dan PostgreSQL: Studi Kasus Yayasan Almadani Sasongko, Agung; Lailiah, Badariatul; Hadikusumah, Prima Surya; Marien, Yunita
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.571

Abstract

This research aims to develop a web-based employee information system for Almadani Syarif Abdurrahman Pontianak Foundation to improve the efficiency of employee data management and facilitate access to employee-related documents. The system was developed using Agile methodology, which enables an adaptive development process through sprint cycles. The Next.js framework was used to improving interface speed, while PostgreSQL was chosen as the database to ensure data integrity and security. Testing results through User Feedback at 9 sprints showed a gradual increase in user satisfaction. The average user assessment scores for key modules, such as employee data management, showed a steady improvement with adjustments made after each sprint, resulting in an average score of 4.14 out of 5. The burn-down diagram showed work progress consistent with the target time, indicating that the development team managed to effectively reduce the backlog until the project was completed on schedule in 45 days. User Acceptance Testing (UAT) revealed high satisfaction levels: 90% for design, 92.5% for ease of use, and 89% for efficiency. System page caching reduced access times by 60-80%, especially on pages with large data loads. Overall, the developed system successfully improved administrative efficiency and streamlined employee data search and management processes.
Sentimen Analisis Photoshop Express di Google Play Store Menggunakan Metode Naive Bayes dan CNN Lailiah, Badariatul; Rizka Dahlia; saadah, Rabiatus
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 1 (2025): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/qd2vme79

Abstract

Technological advancements have brought fundamental changes in the way we interact with digital images and photography. One significant milestone in this development is the Photoshop Express Photo Editor, which has become a primary platform for image processing and editing. Datasets are used to analyze sentiment and are utilized during the accuracy testing phase. Based on the testing results, the Convolutional Neural Network (CNN) algorithm achieved an average accuracy value of 86.50%, compared to the Naïve Bayes (NB) algorithm, which achieved an average accuracy value of 75%. The results of the research conclude that the choice of sentiment analysis method should be tailored to the needs and limitations of the system. If a fast, light, and easy-to-understand process is required, the Naive Bayes method is the right choice. However, if accuracy and context understanding are the top priorities, then CNN is a superior approach, although it requires more resources. Additionally, based on the Wordcloud data, it is known that the majority of comments are positive, indicating that the reviews or texts analyzed contain many positive expressions related to quality, usability, and ease of use.
Peningkatan Literasi Digital melalui Workshop Penulisan Berita di Platform Sosial rumahpintarpunggurcerdas.org Rabiatus Sa'adah; Kartika Handayani; Erni Erni; Badariatul Lailiah
Indonesian Community Service Journal of Computer Science Vol. 3 No. 1 (2026): Periode Januari 2026
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/indocoms.v3i1.10855

Abstract

Kemajuan teknologi informasi dan komunikasi di era digital saat ini telah membawa perubahan besar terhadap cara masyarakat memperoleh dan menyebarkan informasi. Website menjadi salah satu media penting dalam mendukung penyebaran informasi yang cepat, akurat, dan mudah diakses oleh masyarakat luas. Rumah Pintar Punggur Cerdas sebagai pusat kegiatan belajar masyarakat memiliki peluang besar untuk memanfaatkan website sebagai sarana dokumentasi kegiatan, edukasi, serta promosi program sosial. Namun, pemanfaatan website rumahpintarpunggurcerdas.org belum optimal karena keterbatasan kemampuan pengelola dan relawan dalam menulis berita sesuai kaidah jurnalistik. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kemampuan penulisan berita bagi pengelola dan relawan Rumah Pintar Punggur Cerdas melalui Workshop Penulisan Berita untuk Website Sosial RumahPintarPunggurCerdas.org. Metode pelaksanaan dilakukan secara offline melalui pendekatan pelatihan yang meliputi penyampaian materi, diskusi, praktik penulisan, serta evaluasi hasil tulisan. Peserta memperoleh pemahaman tentang teknik penulisan berita, penerapan struktur 5W+1H, penyusunan paragraf efektif, serta praktik publikasi berita ke website dengan bantuan teknologi digital dan Artificial Intelligence (AI). Hasil kegiatan menunjukkan peningkatan kemampuan peserta dalam menulis berita yang lebih sistematis, komunikatif, dan menarik. Peserta mampu menyusun paragraf pembuka yang informatif, menggunakan struktur piramida terbalik, serta menonjolkan unsur human interest yang menggambarkan suasana dan nilai sosial kegiatan. Kegiatan ini berkontribusi nyata dalam meningkatkan literasi digital dan keterampilan jurnalistik peserta, serta mendukung optimalisasi website Rumah Pintar Punggur Cerdas sebagai media komunikasi, edukasi, dan promosi di era digital.
Evaluation of Machine Learning Algorithms in Sentiment Analysis of the Satu Sehat Application Suhendra, Marwan; Lailiah, Badariatul; Yanto, Yanto; Fitriana, Lady Agustin
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1816

Abstract

This study aims to analyze and compare the performance of three sentiment classification algorithms—Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (K-NN)—in classifying user reviews of the Satu Sehat application. The data preprocessing stage involves several steps, including text cleaning through normalization, removal of punctuation, numbers, and irrelevant characters, as well as the elimination of stopwords. Subsequently, stemming is performed to reduce words to their root forms. Feature extraction is conducted using the CountVectorizer method with a bag-of-words approach, which converts textual data into numerical representations. The dataset is then divided into training and testing subsets using an 80:20 train-test split ratio. Model performance is evaluated through a confusion matrix, producing key evaluation metrics such as accuracy, precision, recall, and F1-score. Based on the results of testing 9,192 user reviews, the SVM algorithm with a linear kernel demonstrated the best overall performance compared to NB and K-NN, as indicated by the highest accuracy score. These findings suggest that SVM is more effective in handling high-dimensional textual features, making it a highly suitable algorithm for sentiment analysis of digital health application reviews, particularly those related to Satu Sehat.
Co-Authors Agung Sasongko Ari Abdilah Ari Abdillah Ari Abdillah Asep Sayfulloh Dwi Yuni Utami Erni Erni Erni Erni Fitriana, Lady Agustin Gata, Windu Hadikusumah, Prima Surya Hanggoro Aji Al Kautsar Hendri Mahmud Nawawi Kartika Handayani Laksono Trisnantoro Marien, Yunita Monikka Nur Winnarto Muhammad Ifan Rifani Ihsan Muhammad Rezki Muhammad Sony Maulana Nurmalasari Putri Fitri Astuti Rabiatus Rabiatus Sa'adah Rabiatus Sa'adah Rabiatus Saadah Rabiatus Sa’adah Rabiatus Sa’adah Rabiatus Sa’adah Sa’adah Rangga Pebrianto Reza Maulana Rizka Dahlia Sa'adah, Rabiatus Saadah, Rabiatus Suhendra, Marwan Sujiliani Heristian Verra Sofica Windu Gata YANTO YANTO