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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Simantec POSITIF Sistemasi: Jurnal Sistem Informasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Teknologi dan Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Vokasi Journal of Artificial Intelligence and Engineering Applications (JAIEA) Proceeding Applied Business and Engineering Conference Journal Of Artificial Intelligence And Software Engineering Jurnal INFOTEL Jurnal Sistem Informasi dan Manajemen eProceeding of TIK Jurnal Teknologi Rekayasa Informasi dan Komputer
Arhami, Muhammad
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Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : journal of artificial intelligence and software engineering

 1 
Implementation of the Least Square Method in the Work Plan and Budget Application (SIREKA) at Politeknik Negeri Lhokseumawe Rizqillah, Rizqillah; Arhami, Muhammad; Abdi, Musta’inul; Arifai, Muhammad; Meilvinasvita, Dwi
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6654

Abstract

Every year, all units at the Lhokseumawe State Polytechnic (PNL) are required to plan activity plans and budgets (RKA). This includes Library units, Departments, SPI, P3M, P4M, and other units. However, the process of planning activities to budget realization still depends on Drive services, causing complexity and lack of coordination in managing data. To overcome this problem, a solution is needed in the form of SIREKA (Work Plan and Budget Management Information System). In this application, the least squares method is used as a prediction tool for activity budget data. From the results of the calculations carried out, it is known that the least squares method has a high level of accuracy of 97.71% with a MAPE value of 2.29%, so this method is considered successful. The implementation of SIREKA has been successful in overcoming RKA obstacles in PNL, as well as providing budget predictions that are useful for decision making. SIREKA has proven itself to be a solution that has a positive impact on the Politeknik Negeri Lhokseumawe.
Temporal Pattern Recognition: A BiLSTM-based Framework for Churn Prediction Zulman, Muhammad Reza; Mahmudah, Rifa’atun; Arhami, Muhammad; Davi, Muhammad
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6952

Abstract

Industri telekomunikasi menghadapi tantangan besar dalam mempertahankan basis pelanggannya, di mana churn atau perpindahan pelanggan ke penyedia layanan pesaing menjadi isu krusial yang secara langsung memengaruhi kinerja finansial, efisiensi operasional, serta daya saing perusahaan dalam jangka panjang. Untuk menjawab tantangan ini, diperlukan pendekatan analitik yang mampu memprediksi kemungkinan churn secara akurat. Penelitian ini bertujuan untuk mengevaluasi efektivitas jaringan saraf Bidirectional Long Short-Term Memory (BiLSTM) dalam memprediksi churn pelanggan PT Medianusa Permana melalui analisis data sekuensial temporal. Dataset yang digunakan mencakup data pelanggan dari April 2020 hingga Mei 2023, dengan berbagai variabel prediktif seperti jenis layanan, media transmisi, alokasi bandwidth, status langganan, status kemitraan, ketentuan kontrak, serta riwayat keluhan. Arsitektur BiLSTM yang diterapkan terdiri dari tiga lapisan LSTM bidirectional, dirancang untuk memaksimalkan pengenalan pola temporal sekaligus mengurangi overfitting guna meningkatkan akurasi model. Validasi dilakukan melalui teknik cross-validation dan confusion matrix, yang menunjukkan bahwa model mampu mencapai akurasi rata-rata sebesar 89% serta performa klasifikasi yang tinggi dalam mengidentifikasi pelanggan yang churn maupun tidak churn. Hasil penelitian ini menegaskan bahwa BiLSTM efektif dalam menangkap indikator perilaku halus yang mendahului churn, dan dapat menjadi dasar yang kuat dalam pengembangan strategi retensi pelanggan yang lebih proaktif dan berbasis data.
Rancang Bangun Aplikasi Chatbot Untuk Deteksi Pecandu Pornografi Menggunakan Metode Support Vector Machine (SVM) Al Fath, Muhammad Fajar; Salahuddin, Salahuddin; Arhami, Muhammad
Journal of Artificial Intelligence and Software Engineering Vol 4, No 1 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i1.5399

Abstract

Pornografi adalah representasi seksual dalam media yang menampilkan aktivitas seksual dan bahan yang menekankan pada unsur-unsur seksual yang dapat menyebabkan kecanduan. Salah satu faktor penyebab banyaknya pecandu pornografi di masyarakat karena kurangnya kesadaran dan pemahaman terhadap dampak negatif pornografi. Kesadaran diri akan membantu masyarakat terutama generasi muda untuk dapat meninggalkan konten pornografi dan terhindar dari dampaknya dalam jangka panjang. Penelitian ini bertujuan untuk memberikan solusi bagi para pecandu dan masyarakat untuk menghentikan kebiasaan menikmati pornografi dengan cara membuat Chatbot Porn Addict Detection (ChatPAD) menggunakan metode Support Vector Machine(SVM) melalui pendekatan psikologi PAST(Porn Addiction Screening Tool) dengan model pertanyaan Cyber Pornography Inventory Use. Chatbot memiliki fitur untuk mendeteksi tingkat kecanduan seseorang terhadap pornografi sekaligus memberikan pemahaman terhadap dampak bahaya dan cara berhenti dari adiksi pornografi. Hasil penelitian ini, chatbot berhasil memprediksi tingkat kecanduan pornografi dari para pengguna menggunakan metode SVM  dengan akurasi yang baik dan akurat. Tingkat akurasi yang didapatkan berdasarkan pengujian confusion matrix mencapai 98.3% pada label_2 dan label_3 sedangkan tingkat akurasi terendah terletak pada label_1. Hasil presisi tertinggi dimiliki oleh label_1 dengan angka 98.4% diikuti label_0 sedangkan presisi terendah adalah label_4. Hasil recal tertinggi adalah 87.8% dari label_2 sedangkan terendah adalah 7.14% dari label_1. Hasil f1-score mencatatkan label_2 memiliki hasil tertinggi dengan score 91.1%. Berdasarkan hasil pengujian tersebut maka diperlukan peningkatan performa pada label_1 karena memiliki f1-score dan recall yang rendah.  AbstractPornography is a sexual representation in the media that displays sexual activities and materials that emphasize sexual elements that can cause addiction. One of the factors causing the number of pornography addicts in society is due to a lack of awareness and understanding of the negative effects of pornography. Self-awareness will help the community, especially the younger generation, to be able to leave pornographic content and avoid its impact in the long run. This research aims to provide solutions for addicts and the community to stop the habit of enjoying pornography by creating a Porn Addict Detection Chatbot (ChatPAD) using the Support Vector Machine (SVM) method through the PAST (Porn Addiction Screening Tool) psychology approach with the Cyber Pornography Inventory Use question model. The chatbot has features to detect the level of addiction of a person to pornography as well as provide understanding of the harmful effects and how to stop pornography addiction. The results of this study, the chatbot successfully predicts the level of pornography addiction of users using the SVM method with good and accurate accuracy. The accuracy level obtained based on confusion matrix testing reaches 98.3% on label_2 and label_3 while the lowest accuracy level is located on label_1. The highest precision result is owned by label_1 with 98.4% followed by label_0 while the lowest precision is label_4. The highest recall result is 87.8% from label_2 while the lowest is 7.14% from label_1. The f1-score results recorded label_2 has the highest result with a score of 91.1%. Based on the test results, it is necessary to improve the performance of label_1 because it has a low f1-score and recall.
Co-Authors ., Mursyidah Abdi, Musta’inul Adhimullah, Adhimullah Adriana Adriana Affandi, Azhar Kholiq Agustin, Riya Sri Al Fath, Muhammad Fajar Ali Amran Amirullah -, Amirullah Andhini, Shania Putri Anita Desiani Anwar Anwar Aulia, Annisa Rizka Azhar Azhar Des Alwine Zayanti, Des Alwine Dian Cahyawati Fajriaty, Siti Fauzani, Lia Fitri, Intan Ginting, Rachel Ardana Putra Hendrawati Hendrawati Hendrawaty Hendrawaty, Hendrawaty Henisaniyya, Nabila Hidayat, Hari Toha Huzaeni, Huzaeni Indrawati Indrawati Indri Ramayanti Insan, Jamalul Irmeilyana Isnani Isnani Kanda Januar Miraswan Khadafi, M. Khairunnas, Muhammad Fadil Kurniawati, Devy Mahdi Mahdi, Mahdi Mahmudah, Rifa’atun Masyitha, Masyitha Maulana, Muhammad Andra Mega Fatimah Rosana Meilisa, Dinda Meilvinasvita, Dwi Mesti, Mesti Mortara, Alda Amalia Muakhir, Muakhir Mufida, Nabila Muhammad Arifai Muhammad Davi Muhammad Nasir Muhammad Reza Zulman Muhammad Rizka, Muhammad Mulyadi Mulyadi Muzammil, Muzammil Nahar, Nahar Narti Narti, Narti Nasution, Siti Aisyah Novi Rustiana Dewi Nurakmalinda, Nurakmalinda Permatasari, Mitta Pertiwi, Citra Purnahar, Fadhil Rahmadita, Suristhia Raiyan, Muhammad Rifkie Primartha Rizqillah, Rizqillah Rudi F, Fachri Yanuar Safriadi Safriadi Safriani, Yuni Salahuddin Salahuddin Salahuddin Salahuddin Salnadila, Salnadila Sari, Suci Indah Sasongko, Muhammad Aditya Sitorus, Dina Suzzete Sri Indra Maiyanti Sugandi Yahdin Sugeng Santoso Sukma, Melati Dian Yadi Utama Yassir Yassir Yuliana Yuliana Zahara, Fitria