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All Journal JAIS (Journal of Applied Intelligent System) CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Indonesian Journal of Contemporary Multidisciplinary Research Jurnal Pelita Pengabdian SAINTEK
Amali
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Social Sciences

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Analysis of K-Nearest Neighbor (KNN), Naive Bayes ands Decision Tree C4.5 Algorithm With Classification Method In Breast Cancer Using RapidMiner Iqbal, Muhammad; Donny, Maulana; Wahyu, Hadikristanto; Tedi, Kurniadi Nanang; Amali; Ismasari, Nawangsih
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11242

Abstract

Breast cancer is cancer that forms in the cells of the breast. It is the most common cancer in women and the leading cause of cancer deaths in women worldwide. Breast cancer is usually divided into two types: benign, or usually called benign and malignant, or usually called malignant. Benign cancers are usually characterized by small, round, tender lumps. In the fields of medicine, finance, marketing, and social science, data mining is a popular tool for performing proven analysis. This study will compare K-Nearest Neighbor (KNN), Naive Bayes, and Decision Tree C4.5 approaches for classifying breast cancer. The problem of this research is which algorithm has a high level of accuracy that can be used with breast cancer datasets and can provide information about patterns or models for early detection of breast cancer. The results of the research conducted using CRISP-DM show that K-Nearest Neighbor (KNN) has the highest accuracy value with 97.14% and its AUC value is 0.976. The AUC value also showed excellent classification, with an AUC value between 0.90 and 1.00.
Survei Pohon Keputusan Entropi untuk Memprediksi Kematangan Buah Durian Varietas Musangking Maulana, Donny; Amali
Prosiding Sains dan Teknologi Vol. 3 No. 1 (2024): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 3 - Januari 2024
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Kematangan buah durian varietas Musangking merupakan faktor penting yang menentukan kualitas dan harga jualnya. Oleh karena itu, prediksi kematangan buah durian menjadi penting untuk dilakukan. Dalam penelitian ini, metode pohon keputusan entropi digunakan untuk memprediksi kematangan buah durian varietas Musangking. Data yang digunakan berupa data hasil pengukuran karakteristik fisik dan kimia buah durian, yaitu warna kulit, ketebalan kulit, bobot buah, kadar air, dan kadar gula. Hasil penelitian menunjukkan bahwa pohon keputusan entropi dapat digunakan untuk memprediksi kematangan buah durian varietas Musangking dengan akurasi sebesar 90%.
Pengembangan Technopreneur Pasca PHK melalui Pelatihan Penanaman Anggur dan Pemanfaatan Teknologi ChatGPT di Perum Cikarang Baru Desa Jayamukti Maulana, Donny; Amali; Anggi Muhammad Rifa’i; Ismasari Nawangsih; Miftakul Huda
Jurnal Pelita Pengabdian Vol. 3 No. 1 (2025): Januari
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Tingginya angka Pemutusan Hubungan Kerja (PHK) di sektor manufaktur akibat pandemi COVID-19 telah memberikan dampak signifikan bagi masyarakat Desa Jayamukti, Perum Cikarang Baru. Untuk menghadapi tantangan ini, penelitian ini menghadirkan solusi inovatif dengan mengembangkan technopreneurship berbasis agribisnis dan teknologi digital. Program ini menawarkan pelatihan budidaya anggur sebagai peluang usaha baru yang bernilai ekonomis, serta pemanfaatan kecerdasan buatan melalui teknologi ChatGPT untuk mendukung strategi pemasaran dan manajemen usaha. Melalui pendekatan holistik, program ini tidak hanya membekali masyarakat dengan keterampilan teknis dalam pertanian perkotaan, tetapi juga mendorong transformasi digital yang dapat memperluas akses pasar dan meningkatkan daya saing usaha. Dengan adanya dukungan dari kebijakan Merdeka Belajar Kampus Merdeka (MBKM), kegiatan ini diharapkan dapat memperkuat kolaborasi antara akademisi dan masyarakat dalam menciptakan solusi yang berkelanjutan. Pada akhirnya, program ini bertujuan untuk menciptakan kemandirian ekonomi, mendorong jiwa wirausaha berbasis teknologi, serta membuka peluang baru di sektor agribisnis yang inovatif dan adaptif terhadap perubahan zaman.
PENERAPAN DECISION TREE DALAM MENDETEKSI POLA TINGKAT STRESS MANUSIA BERDASARKAN POLA TIDUR MENGGUNAKAN RAPID MINER Ahmad shofwan anshory; Amali; Fauzhan Qhof Pratama; Ridho Pikriyansyah
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i2.4311

Abstract

According to data from the Health Service Monitor in 2023, stress is one of the most worrying health problems for 30% of respondents. Stress is often associated with sleep patterns. This study aims to identify the relationship between sleep patterns and stress levels in humans using 10 levels: 1-2 (normal), 3-4 (mild), 5-6 (moderate), 7-8 (high), 9-10 (very high). The model used in this study is decision tree, with data covering gender, age, occupation, sleep quality, physical activity level, BMI (Body Mass Index) category, blood pressure, heart rate, daily activities, and sleep disorders. This study is expected to provide valuable information on the relationship between sleep patterns and stress, so that strategies can be developed to improve sleep quality and reduce stress. Based on the data analysis, there are several factors that cause increased stress levels, namely blood pressure, sleep quality, body weight, gender, and daily activities. This can have a significant impact on sleep quality.
A Analisis Sentimen Ulasan Pengguna Aplikasi Tokopedia Berbasis Algoritma Naive Bayes Serta Pendekatan Klasifikasi Sentimen Positif Dan Negatif: - Pendahuluan, Metodelogi penelitian , Hasil dan pembahasan , kesimpulan Aditiya, Rangga; Riffani, Sidik; Maulana, Faris; Amali
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i2.4320

Abstract

Changes in consumer behavior in shopping for daily necessities have driven the growth of e-commerce applications as the main platform for transactions. Tokopedia, as one of the leading e-commerce applications in Indonesia with a large number of users, is the main focus of this study. This study aims to analyze the sentiment of Tokopedia user reviews published on the Google Play Store using a data mining approach with Naive Bayes algorithm.Positive and negative sentiment classification methods are used to understand the views and evaluations of users on Tokopedia services. Review Data is extracted, processed, and trained using the Naive Bayes algorithm to classify reviews into positive or negative sentiments.
Penerapan Algoritma Decision Tree untuk Prediksi Kelulusan Mahasiswa Berdasarkan Data Akademik Menggunakan RapidMiner Laela Nur Rohmah; Sara Khusnul Mumtazah; Alvina Damayanti; Amali
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i1.4332

Abstract

Higher education has an important role in the long-term development of each individual. One of the most important indicators of success for a high-performing educational institution is student achievement. There are several factors that might influence student achievement, including academic, demographic, and socioeconomic factors. This study employs the Decision Tree algorithm, which is one of several effective algorithms for making predictions or analyzing large amounts of data. This study aims to determine whether the Decision Tree algorithm can be used to predict student achievement by gathering information on accuracy, precision, and recall obtained during data collection. This study used RapidMiner tools to create a Decision Tree model and was carried out with the following steps: data collection, data analysis, Decision Tree modeling, method development, and results evaluation. Data collection on the dataset will be divided into two parts: 70% for training and 30% testing. The results of the study on the decision tree algorithm show that it has a good performance with a high accuracy of 73.17%. It also performs well in predicting graduate students with a precision of 74.05% and a recall of 93.82%, as well as dropout students with a precision of 73.02% and a recall of 80.05%.
Pendekatan Klasterisasi K-Means untuk Mengkategorikan Risiko Obesitas dengan Rapidminer Rahman Santosa, Ravansa; Amali; Sasi Kirana, Anindia; Muhana Aydin Nashif, Hamim
Jurnal SIGMA Vol 15 No 1 (2024): Juni 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i1.4660

Abstract

Millions of people around the world have obesity problems, which increase the risk of various chronic diseases. Clustering method with k-means algorithm is used in this study to analyze obesity patterns based on behavioral and physical data. The obesity risk data was processed using RapidMiner after being obtained from the Kaggle source. The “Nominal to Numerical” operator converts nominal attributes into numerical data, which allows the k-means algorithm to be used. The elbow method was used to select the ideal number of clusters. The clustering results identified three main groups based on healthy lifestyle, high obesity risk, and different levels of physical activity. This analysis improves our understanding of obesity patterns and the factors that contribute to the condition. The results from this study can help in the creation of better prevention and intervention methods to effectively address obesity.
Analisis Sentimen Pada Teknologi 5G Menggunakan Algoritma Random Forest dan Naïve Bayes dengan Dataset Multibahasa Muhammad Alwi Nur Fathihah; Amali; Majid, Annisa Maulana
CESS (Journal of Computer Engineering, System and Science) Vol. 10 No. 2 (2025): Juli 2025
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v10i2.66502

Abstract

Perkembangan teknologi 5G sebagai generasi terbaru jaringan nirkabel telah menimbulkan beragam tanggapan publik, baik yang mendukung maupun yang menolak. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap teknologi 5G berdasarkan komentar pengguna YouTube dalam bahasa Indonesia dan Inggris. Data diperoleh menggunakan teknik web crawling, kemudian diproses melalui tahapan SEMMA, yang mencakup preprocessing, pelabelan sentimen, dan pelatihan model. Dua algoritma yang digunakan adalah Random Forest dan Naïve Bayes. Evaluasi dilakukan menggunakan confusion matrix dan metrik seperti akurasi, precision, recall, dan F1-score. Hasil menunjukkan bahwa Random Forest memiliki performa yang lebih baik dengan akurasi 94,8% dan mampu mengklasifikasikan sentimen positif dan negatif secara seimbang. Sementara itu, Naïve Bayes cenderung bias terhadap sentimen positif dan memiliki kelemahan dalam mendeteksi komentar negatif. Penelitian ini menunjukkan bahwa Random Forest lebih andal untuk analisis sentimen multibahasa, khususnya dalam konteks opini publik terhadap teknologi 5G.
Sharia Marketing in Business: Paradigm, Ethics and Implementation of Islamic Principles Hidayat, Chaerul; Rini Ariza; Aan Fadillah; Muhammad Reza; Amali
Indonesian Journal of Contemporary Multidisciplinary Research Vol. 3 No. 4 (2024): July 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/modern.v3i4.9785

Abstract

Marketing is the main focus for companies in their efforts to develop and maintain their business. This article reviews in detail the concept of marketing in a general context and sharia marketing. Sharia marketing emphasizes Islamic moral principles such as faith, khalifah, balance, and justice, which form the basis for the strategy, tactics, values, and image of Islamic marketing. In addition, in practice, Islamic marketing also includes aspects of spirituality, considering competition as a partner to spur creativity. Specific characteristics of Islamic marketing include unity, faith, balance, justice, freedom of will, and benevolence. Islamic marketing ethics emphasize integrity, honesty, and good service. It is hoped that by applying these principles, companies can create a healthy business environment and provide benefits for all parties involved
Utilization of Media and Technology in Learning Islamic Religious Education Amrullah, Hilman Ihza; Alif Nur Fathlii Amarta; Taufik Qurhahman; Amali
Indonesian Journal of Contemporary Multidisciplinary Research Vol. 3 No. 4 (2024): July 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/modern.v3i4.10010

Abstract

This research explores the use of media and technology in learning Islamic Religious Education in the digital era. The application of modern technology and interactive media has the potential to improve the quality and effectiveness of the PAI learning process. This study will analyze various technological platforms and tools used to deliver PAI material to students. Digital media such as learning videos, mobile applications and e-learning platforms can help improve students' understanding of PAI material. Apart from that, the use of technology also facilitates more interactive and interesting learning, and supports independent learning. This study also emphasizes the importance of educators' readiness to integrate technology into the PAI curriculum and the need for training and support for teachers. Proper use of technological media will bring positive changes in Islamic Religious Education learning
Co-Authors Aan Fadillah Aditiya, Rangga Ahmad shofwan anshory Alif Nur Fathlii Amarta Alvina Damayanti Amrullah, Hilman Ihza Anggi Muhammad Rifa’i Donny, Maulana Fauzhan Qhof Pratama Hidayat, Chaerul Ismasari, Nawangsih Laela Nur Rohmah Majid, Annisa Maulana Maulana, Donny Maulana, Faris Miftakul Huda Muhammad Alwi Nur Fathihah Muhammad Reza Muhana Aydin Nashif, Hamim Nawangsih, Ismasari Rahman Santosa, Ravansa Ridho Pikriyansyah Riffani, Sidik Rini Ariza Sara Khusnul Mumtazah Sasi Kirana, Anindia Sugeng, Santoso Taufik Qurhahman Tedi, Kurniadi Nanang Wahyu, Hadikristanto