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All Journal SEIKO : Journal of Management & Business JUKI : Jurnal Komputer dan Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) Repeater: Publikasi Teknik Informatika dan Jaringan Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Saturnus: Jurnal Teknologi dan Sistem Informasi
Muammar Khadapi
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Automotive Engineering Computer Science & IT Control & Systems Engineering Social Sciences

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Pengaruh Gaya kepemimpinan dan disiplin kerja Terhadap Kinerja Karyawan PT. Tuuk Maju Bersama Kota Palopo Usman Usman; Saharuddin Saharuddin; Muammar Khadapi
SEIKO : Journal of Management & Business Vol 6, No 1 (2023): January - Juny
Publisher : Program Pascasarjana STIE Amkop Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/sejaman.v6i1.3764

Abstract

Penelitian ini untuk melihat dan menganalisis Pengaruh Gaya kepemimpinan dan disiplin kerja Terhadap Kinerja Karyawan. Dimana sampel yang dijadikan responden yaitu sebanyak 50 karyawan. Teknik pengumpulan data yang digunakan yaitu dengan membagikan kuesioner kepada responden. Metode penelitian yang di gunakan adalah regresi linier berganda. Dimana hasil penelitiannya gaya kepemimpinan berpengaruh positif signifikan terhadap retensi pegawai. Hal tersebut ditunjukkan pada uji t bahwa nilai prob. Gaya kepemimpinan lebih kecil dari taraf alpha 5%. Dengan demikian menunjukkan bahwa gaya kepemimpinan yang di terima oleh karyawan sangat berpengaruh pada kinerja karyawan. Dimana hasil penelitiannya disiplin kerja berpengaruh positif signifikan terhadap kinerja karyawan. Hal tersebut ditunjukkan pada uji t bahwa nilai prob. Disiplin lebih kecil dari taraf alpha 5%. Dengan demikian menunjukkan bahwa disiplin kerja yang di terima oleh karyawan sangat berpengaruh pada kinerja karyawan Kata Kunci: gaya kepemimpinan, Dan disiplin kerja Terhadap kinerja karyawan
Classification of Tile Productivity Data Based on Tile Type Using Random Forest Algorithm in Langkat Regency Muhammad Arif Ridho; Buaton, Relita; Muammar Khadapi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 1 (2024): October 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i1.670

Abstract

This study aims to classify data on the productivity of census in Langkat Regency based on the type of census by using the Random Forest algorithm. Ubinan is a method used to measure the productivity of food crops, and in this study, the data was processed with various variables such as planting area, type of fertilizer, type of pesticide, and production volume. The Random Forest algorithm was used to build a classification model that could predict the productivity of the tares with very high accuracy, reaching 99.58% in the training stage. The model categorizes the productivity of the samples into several levels, namely Very Low, Low, Medium, High, and Very High. The implementation of this system is also equipped with a MATLAB GUI interface, which makes it easier for users to train and test data efficiently. With this system, users can see the prediction results through intuitive visualization. This research is expected to help farmers and policy makers in improving agricultural productivity through data-based analysis.
Analisis Sentimen Berbasis Jaringan LSTM dan BERT terhadap Diskusi Twitter tentang Pemilu 2024 Muammar Khadapi; Pakpahan, Victor Maruli
JUKI : Jurnal Komputer dan Informatika Vol. 6 No. 2 (2024): JUKI : Jurnal Komputer dan Informatika, Edisi Nopember 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemilihan Umum (Pemilu) merupakan peristiwa politik penting yang memicu banyak diskusi di media sosial, terutama di platform seperti Twitter. Analisis sentimen dari diskusi ini dapat memberikan wawasan mengenai pandangan masyarakat terhadap calon, partai, serta isu-isu yang terkait. Penelitian ini berfokus pada penerapan dua model deep learning, yaitu Long Short-Term Memory (LSTM) dan Bidirectional Encoder Representations from Transformers (BERT), untuk menganalisis sentimen diskusi Twitter tentang Pemilu 2024. Kedua model ini dipilih karena kemampuan mereka dalam menangani data teks yang kompleks dan konteks bahasa alami. Dataset yang digunakan dalam penelitian ini terdiri dari ribuan tweet terkait Pemilu 2024, yang diklasifikasikan ke dalam tiga kategori sentimen, yaitu positif, negatif, dan netral. Data terlebih dahulu diproses melalui tahap pembersihan teks dan tokenisasi. Model LSTM dan BERT dilatih menggunakan dataset ini untuk memprediksi sentimen dengan fokus pada peningkatan akurasi prediksi. Hasil eksperimen menunjukkan bahwa model BERT secara konsisten memberikan performa yang lebih baik dibandingkan dengan LSTM. Model BERT berhasil mencapai akurasi validasi sebesar 76.48% pada epoch kedua, sedangkan model LSTM hanya mencapai akurasi maksimal 87 %. Meskipun demikian, model BERT mulai menunjukkan gejala overfitting pada epoch ketiga, dengan peningkatan nilai loss pada data validasi. Hal ini menunjukkan bahwa tuning lebih lanjut pada hyperparameter seperti jumlah epoch dan learning rate diperlukan untuk meningkatkan generalisasi model. Sementara itu, model LSTM menunjukkan stabilitas yang lebih baik, meskipun akurasinya lebih rendah, terutama dalam menangani dependensi konteks yang lebih sederhana. Secara keseluruhan, penelitian ini menegaskan bahwa model BERT lebih efektif dalam menangkap konteks kompleks pada teks Twitter terkait Pemilu 2024 dibandingkan dengan LSTM. Namun, tantangan seperti overfitting dan optimasi hyperparameter tetap menjadi perhatian utama. Untuk meningkatkan performa lebih lanjut, perlu dipertimbangkan teknik augmentasi data dan tuning hyperparameter yang lebih optimal. Penelitian ini juga membuka peluang untuk pengembangan model hibrida yang menggabungkan keunggulan LSTM dan BERT dalam analisis sentimen berbasis teks.
Analisis Sentimen Masyarakat terhadap Program Makan Siang Gratis di Indonesia Tahun 2024 Menggunakan Long Short-Term Memory (LSTM) Silvia Amara; Novriyenni, Novriyenni; Muammar Khadapi
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 4 (2025): Juli : Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i4.930

Abstract

The free lunch program is a goverment initiative aimed at addressing the issue of stunting in Indonesia. This program focuses on toddlers, school-age children and pregnant women. Various opinions have emerged from the public regarding this initiative, especially through sosial media platform X (Twitter) and news portals. In this research, sentiment analysis was conducted to understand public responses to the program, whether they are positive, neutral or negative. To evaluate the accuracy of the sentiment analysis perfomed, a deep learning approach was applied using the Long Short-Term Memory (LSTM) algorithm. The results show that public sentiment varies responses, on social media X tend to be negative, while those on news portals tend to be positive toward the free lunch program in Indonesia. Through LSTM-based testing, sentiment analysis on tweet data achieved an accuracy of 88.6%, with a precision of 84.6%, recall of 88.6% and an F1-Score of 86.3%. Meanwhile, sentiment analysis on news portal data reached an accuracy of 89%, with a precision of 81.7%, recall of 89% and an F1-Score of 85.1%.
Pengelompokan Tindak Kejahatan Berdasarkan Tempat Kejadian Perkara di Kota Binjai Menggunakan Metode Clustering : Studi kasus: Polres Binjai Herdina Putri Ahmadi; Magdalena Simanjuntak; Muammar Khadapi
Saturnus : Jurnal Teknologi dan Sistem Informasi Vol. 3 No. 3 (2025): Juli : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v3i3.933

Abstract

Crime is a social issue that continues to evolve alongside increasing community activity and regional development. This study aims to Cluster crime data in Binjai City based on the location of incidents using the K-Means algorithm and the Cross Industry Standard Process for Data Mining (CRISP-DM) approach. The data were obtained from the Binjai Police Department, with attributes including the type of crime, time of occurrence, and location, categorized by district. A comprehensive data preprocessing stage was carried out, involving the extraction of information from raw data, normalization of crime type labels, and conversion of categorical data into numerical form using label encoding. The optimal number of Clusters was determined using the Silhouette score method, which yielded the best result at K = 10. The Clustering results were further evaluated using the Davies-Bouldin Index (DBI) to ensure Cluster quality. The analysis revealed that Binjai Utara District has the highest number of crimes, particularly aggravated theft (curat), which frequently occurs from early morning to late morning. This Clustering is expected to provide valuable insights for authorities in formulating more targeted and data-driven regional security strategies.
Rancang Bangun Peminjaman Buku pada Perpustakaan STMIK Kaputama Menggunakan RFID Berbasis IoT Assya Harnita Lubis; Husnul Khair; Muammar Khadapi
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 3 (2025): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v3i3.615

Abstract

The rapid advancement of information technology has brought significant transformation across multiple sectors, including library management systems. One of the pressing needs in higher education libraries is the development of an efficient, accurate, and secure borrowing system that reduces human error and improves service quality. This study aims to design and implement a book borrowing system for the STMIK Kaputama library by utilizing Radio Frequency Identification (RFID) technology integrated with the Internet of Things (IoT). The proposed system employs the ESP32 microcontroller as the core controller, supported by the RFID RC522 module and buzzer as primary devices to facilitate the automatic identification of library members and borrowed books. The borrowing and returning process is executed through RFID card scanning, with all transaction data transmitted via WiFi and stored in a MySQL database, which is managed through a PHP-based web platform. The development process follows the Agile methodology, enabling iterative improvement and adaptability to user requirements. System testing demonstrates that the integration of RFID and IoT technology significantly enhances the efficiency, accuracy, and speed of library operations compared to the previous manual system. Furthermore, the system allows real-time recording of borrowing transactions, monitoring of user activities, and centralized data management. These features not only streamline library services but also improve data security and reduce the risk of loss or duplication of records. Overall, the implementation of this RFID-IoT-based system provides an innovative solution for modernizing library management, particularly in higher education institutions. The system ensures faster services, minimizes errors, and creates a structured and reliable digital infrastructure to support academic information services. This study highlights the potential of combining RFID and IoT technologies to improve the quality and effectiveness of library systems in the digital era.
Diagnosa Penyakit Syndrome pada Anak menggunakan Metode Case Base Reasoning (CBR) Amysa Putri Sitepu; Novriyenni Novriyenni; Muammar Khadapi
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 6 (2025): November: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i6.1139

Abstract

Syndrome is a serious problem in children's health because it has a major impact on growth and development, especially in terms of intelligence and daily activities. Down Syndrome, as one of the most well-known chromosomal disorders, is often the main cause of intellectual developmental disorders, hypotonia, facial dysmorphism, early onset of Alzheimer's disease, and various behavioral disorders. Diagnosing syndrome diseases in children is often difficult due to complex and varied symptoms, requiring lengthy, costly, and time-consuming medical evaluations. This study aims to design a Case-Based Reasoning (CBR)-based expert system for diagnosing syndromes in children, which is expected to help accelerate the disease identification process and provide more effective and efficient solutions. The method used is the development of an expert system with a CBR approach, in which the system performs calculations and matching based on the symptoms selected by the user against the available case base. The results of the study show that from symptom inputs such as wide hands with short fingers, short stature, small head, stunted growth, small lower jaw, abnormal body appearance, and weak joints, the system was able to diagnose Klinefelter syndrome with a percentage of 43.58%. This system can be an alternative for patients or families who have limited time and funds to obtain medical consultations, so that diagnosis and follow-up can be carried out more quickly and efficiently.
IMPLEMENTATION OF THE SPIRAL METHOD FOR ANALYZING AND DESIGNING FINANCIAL INFORMATION SYSTEMS AND FINANCIAL ARCHIVES FOR CASHIER FINANCIAL MANAGEMENT SECTION (CASH INFORMATION REPLACEMENT) Muammar Khadapi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 2 No. 2 (2023): February 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v2i2.160

Abstract

This title is backgrounded by financial management employees in the PT Telekomunikasi Indonesia.Tbk Cooperative in the form of Application.There are many problems happened such as the data did not saved well and the financial data was mixed with the other archive. The purpose of this research is to build a cashier application system which will enumerate employees in its financial arrangement, both income and from the cooperative and then become a file which is computerized will facilitate the employees. The methods which is used in this application development method is Spiral. It is the systematic approach and sequentially to software, start from users’ specification necessary until the planning, modeling, construction, and deployment. After analyzing the problems that occur then made the improvement to the current problem by build an application that supports web-based financial processes.
Co-Authors Amysa Putri Sitepu Assya Harnita Lubis Buaton, Relita Herdina Putri Ahmadi Khair, Husnul Magdalena Simanjuntak Muhammad Arif Ridho Novriyenni Novriyenni Novriyenni, Novriyenni Pakpahan, Victor Maruli Saharuddin Silvia Amara Usman Usman