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All Journal Jurnal Inotera Jurnal Informatika Universitas Pamulang Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Artificial Intelligence and Innovative Applications (JOAIIA) KOMMAS: Jurnal Pengabdian Kepada Masyarakat Abdi Jurnal Publikasi Journal of Research and Publication Innovation LOGIC : Jurnal Ilmu Komputer dan Pendidikan Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia OKTAL : Jurnal Ilmu Komputer dan Sains JRIIN :Jurnal Riset Informatika dan Inovasi TEKNOBIS : Jurnal Teknologi, Bisnis dan Pendidikan Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Endar Nirmala
Universitas Pamulang
Humanities Astronomy Chemistry 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 Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Social Sciences Transportation Other

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Implementation of Pivot Table Analysis to Identify Sales Trends in E-Commerce Business at SMK Al Amanah Nirmala, Endar; Mulyati, Sri
KOMMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 1 (2026): KOMMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : KOMMAS: Jurnal Pengabdian Kepada Masyarakat

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Abstract

This Community Service activity was conducted at SMK Al Amanah as an effort to enhance participants’ abilities in processing and analyzing sales data in e-commerce businesses. The available sales data were generally used only as archives, thus providing limited valuable information for business strategy development. Therefore, this activity aimed to improve students’ understanding and skills in processing and analyzing e-commerce sales data through the application of Pivot Table analysis. The implementation method included a preparation stage, the delivery of materials on e-commerce concepts and data analysis, hands-on practice in creating and using Pivot Tables with spreadsheet applications, as well as mentoring and discussions. Sales datasets were used as practice materials to provide participants with real experience in grouping and summarizing data based on specific variables, such as time, products, and sales volume. The results showed that participants were able to independently create Pivot Tables, understand sales patterns and trends, and identify best-selling products and peak sales periods. In addition, the activity enhanced participants’ analytical thinking skills and data literacy in the context of digital business. Participants developed a stronger understanding of the importance of data analysis as a basis for decision-making in e-commerce businesses. In conclusion, the application of Pivot Table analysis in this Community Service activity was effective in improving the competencies of SMK Al Amanah students in data processing and e-commerce business, as well as in supporting data-driven learning.
Comparison of LSTM and Naïve Bayes in Google Play Store App Review Sentiment Analysis Endar Nirmala; Andri Fahmi
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID653

Abstract

The development of mobile application technology has driven increased user interaction through digital reviews on the Google Play Store platform. The review contains opinions that reflect the user's level of satisfaction, experience, and complaints about the app. However, the large number of reviews and variations in language expressions make manual analysis inefficient and potentially subjective. The main problem in this study is how to determine the most effective sentiment classification model to accurately identify users emotional tendencies. This study aims to compare the performance of the Naive Bayes method as a conventional machine learning model with Long Short Term Memory (LSTM) as a deep learning model based on word order in analyzing the sentiment of user reviews of Google Play Store applications. The dataset used comes from Google Play Store Reviews and goes through a pre-process process that includes text cleanup, tokenization, stopword removal, and sentiment labeling based on rating scales. The Naive Bayes model is trained using the TF-IDF representation, while the LSTM model uses an embedding sequence with standardized input padding. Evaluation uses accuracy metrics and F1-score with a ratio of 80 : 20 to train and test data distribution. The test results showed that the Naïve Bayes model achieved an accuracy of 65.78% with an F1 score of 0.5589, while the LSTM only achieved an accuracy of 45.26% with an F1-score of 0.2077. Thus, Naive Bayes was established as the best model in this study
Implementasi Metode Analytical Hierarchy Process (AHP) Untuk Evaluasi dan Pemilihan Karyawan Terbaik Berdasarkan Kinerja dan Kompetensi Studi Kasus Burger KiNG Cipondoh Raya Anggi Pradana Yoani; Endar Nirmala
Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Vol. 7 No. 1 (2026): February
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/joaiia.v7i1.57485

Abstract

Human resources are a crucial asset in supporting organizational success, particularly in the fast-food restaurant industry, which demands optimal employee performance and competence. However, employee evaluation processes that are still conducted manually tend to cause subjectivity and inconsistency in decision-making. This study aims to design and implement a decision support system to evaluate and determine the best employee at Burger King Cipondoh Raya using the Analytical Hierarchy Process (AHP) method. The AHP method was selected because it is capable of decomposing complex problems into a hierarchical structure and producing objective priority weights through pairwise comparisons. The evaluation criteria consist of two main aspects, namely performance, which includes responsibility, productivity, and work discipline, and competence, which covers knowledge, skills, and work attitude. The system was developed as a web-based application using the PHP programming language and MySQL as the database, following the waterfall development model. The data processing results indicate that the system is able to generate an objective employee ranking, in which the employee with the highest preference value obtained a final score of 0.449, and was therefore determined as the best employee. These results demonstrate that the implementation of the AHP method can assist management in conducting employee evaluations in a more accurate, fair, and measurable manner, as well as supporting accountable managerial decision-making.
Implementasi MFEP dan AHP untuk Penentuan Smartphone Terbaik Berbasis Web Studi Kasus: Findesta Cell Muhammad Reza Fadilah; Endar Nirmala
Jurnal Riset Informatika dan Inovasi Vol 3 No 10 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

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Abstract

Penelitian ini membahas pengembangan Sistem Pendukung Keputusan (SPK) berbasis web untuk membantu konsumen dalam menentukan pilihan smartphone terbaik di Findesta Cell. Permasalahan yang sering muncul adalah banyaknya pengguna yang kebingungan dalam memilih smartphone sesuai dengan kebutuhan dan anggaran yang dimilikinya, mengingat beragamnya spesifikasi dan harga yang ditawarkan di pasaran. Untuk menyelesaikan masalah tersebut, penelitian ini menggabungkan dua metode, yaitu Multi Factor Evaluation Process (MFEP) dan Analytical Hierarchy Process (AHP). Metode AHP digunakan untuk memberikan bobot pada setiap kriteria yang relevan seperti spesifikasi teknis, kualitas kamera, kapasitas baterai, harga, dan aspek penting lainnya. Selanjutnya, metode MFEP digunakan untuk melakukan proses evaluasi dan perangkingan alternatif smartphone berdasarkan bobot yang telah ditentukan. Melalui platform web, pengguna dapat memasukkan preferensi dan kebutuhan mereka, yang kemudian diolah oleh sistem untuk menghasilkan rekomendasi smartphone paling sesuai. Hasil implementasi menunjukkan bahwa sistem ini mampu memberikan rekomendasi yang akurat dan relevan dengan kebutuhan serta anggaran pengguna. Dengan adanya SPK ini, konsumen memperoleh panduan yang lebih tepat dan efisien dalam proses pembelian smartphone, sekaligus meningkatkan pengalaman belanja di Findesta Cell.
Analisa Metode Data Mining Untuk Mengelompokkan Transaksi Nasabah Prioritas Dengan Algoritma Naive Bayes Dalam Dunia Perbankan Muhammad Riswan; Endar Nirmala
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 6 No. 2 (2026): Juli : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v6i2.10510

Abstract

significant increase in the volume of customer transaction data, but its utilization as a basis for strategic decision-making remains suboptimal. Priority customers are a high-value segment that requires a deep understanding of transaction patterns to support improved service quality and loyalty. This study aims to analyze the application of data mining methods using the Naive Bayes algorithm to classify priority customer transactions at PT. CIMB Niaga into Silver, Gold, Platinum, and Black categories. The research methods used include literature review, secondary data collection in the form of priority customer transaction data, including transaction frequency, transaction type, nominal amount, and balance, requirements analysis, system design, implementation, and system testing. The dataset used consists of a number of priority customer transaction data simulated to resemble real banking conditions. The results show that the Naive Bayes algorithm is capable of classifying priority customer transactions effectively and efficiently with a sufficient level of accuracy and relatively fast computation time. The developed system is able to display classification results in a structured manner and supports analysis of customer transaction behavior. The conclusion of this study indicates that applying data mining with the Naive Bayes algorithm can be a solution to support strategic decision-making, particularly in priority customer segmentation, improving service quality, and banking operational efficiency.  
Co-Authors Abdullah Syafii Abdullah Syafii Abdurrohman, Fikri Agung Mursito Ahmad Darmawan Ahmad Irkham Ahmad Muhlisin Ahmad Muhlisin Ahmad Rizal ahmad rizal Ainurrohim, Reji Ajeng Rohmatun Nazilah Akbar, Faris Aldi Maulana Alexius Grennantoro Aliansyah, Andi Alpiansyah, Rizki Amirulloh, Yuslifar Khalif Ananta Dicapriyo Andi Lisdiarto Andi Widiarto Andreas, Elbino Andri Fahmi Andris, Ziska Anggi Pradana Yoani Anggi Pradana Yoani Apriliyan Mahardika S. Aqidatul Izzah Chairul Ardiansyah, Muhammad Fadil Ari Mulyoto Aries Saifudin Arivanza Yuke Pradikta Asep Sucipto Indra Sukma Ayu Eka Hapsari Q. Bisri Ali, Faisal Chaesar, Rizky Chairul, Aqidatul Izzah Damanik, Dearma Alam Desi Jasmiati Dicapriyo, Ananta Dicky Wahyudi Dilan Tri Ovandi Dola Irwanto Dwi Nur Febrianto Eko Febriansyah Elbino Andreas Eltyes Michael Efata Zebua Erna, Fadil Ersa Putri Muharom Eva Fauziah Eva Fauziah Fadhilla, Khairani Faris Akbar Fathul Ghina Fauzi, Muhamad Imam Fernando David Hence Rotty Fikri Rahardian Firmansyah Firmansyah Firmansyah Firmansyah Fitri Anis Isroriyah Fransisco Fransisco Fransisco, Fransisco Frennandi Ade Ilyas Galih Maulana Ismail Hadian Hibatul Wafi, M. Hairul Ridwan Hernanda Anggara Putra Hibban, Muhammad Ibnu Ilyas, Frennandi Ade Indrawan, Dicky Irkham, Ahmad Irpan Kusyadi Ismail, Galih Maulana Jasmiati, Desi Jejen Juanda Jenau, Efrida Juanda, Jejen Juhaendi Juhaendi Jumadi Jumadi Jumadi Jumadi Junita, Nurma Khairani Fadhilla Khusnul Salbiah, Siti Kurniasari, Sischa Leonard Viffo Lukita, Muhammad Bagas Ma'ani, Muhammad Lizam Maria Lusiana V Mazok Marjuki Marjuki Mirza Akrom Nunsyah Misbahuddin, Muhammad Rafif Mohammad Kevin Putra Adiyaksa Mohammad Zaeni moza malik Muhajar, Aldy Muhamad Imam Fauzi Muhammad Cahya Rifqi Muhammad Ibnu Hibban Muhammad Lizam Ma'ani Muhammad Rafif Misbahuddin Muhammad Reza Fadilah Muhammad Riswan muhammad rizky, muhammad Muhammad Taufiq Kamaludin Munawaroh Munawaroh Mursito, Agung Nazilah, Ajeng Rohmatun Niki Ratama Noris, Shandi Nurdin, Rachmat Septian Nurhidayat, Rizki Nurrudin, Naufal Nursafitri, Rika Oktavianto, Ricky Nur Ovandi, Dilan Tri Padlo Maldini Pandu Wicaksono, Daffa Putri Aprillia, Cikal Putri, Septi Nur Ilmi Rachmat Septian Nurdin Raden Wirawan Kukuh Pambudi Rahardian, Fikri Rahmayanti, Nabila Eka Ramdhani, M. Aryo Rangga Pradita Nurdin Redha Juliansyah Reji Ainurrohim Ricky Nur Oktavianto Rika Nursafitri Rivansyah Rivansyah Rizki Restu Riyadi Rizky Chaesar Rizky Maulana, Muhammad Rizky Tiwa Saputra Rosa Ardiani Rotty, Fernando David Hence Saputra, Ripki Adi Selly Nur Holifah Septi Nur Ilmi Putri Sischa Kurniasari Siti Marifah Sri Mulyati Sri Mulyati Sri Mulyati Suranta, Rahmatdin Teti Desyani Umul Musfiroh Nurali Vyka Septiani Wahyu Satrio Rizki Wahyudi, Wahyudin Wahyudin Wahyudi Widiarto, Andi Yanto, Galih Ferdhi Yasmin, Afifah Yoga Fahreza Yogi Afandi Yulianti Yulianti Yulianti Zaeni, Mohammad Ziska Andris