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All Journal JFA (Jurnal Fisika dan Aplikasinya) Jurnal Informatika Universitas Pamulang Jurnal Riset Informatika M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika JTIM : Jurnal Teknologi Informasi dan Multimedia Jurnal Sains dan Teknologi
Achmad Hindasyah, Achmad
Pusat Penelitian Bahan Industri Nuklir (PTBIN) - BATAN
Computer Science & IT Economics, Econometrics & Finance Education Mathematics Physics Social Sciences

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Program Simulasi Perancangan Rangkaian Magnetik Pembangkit Medan Elektromagnet untuk Pengujian Sensor GMR Hindasyah, Achmad; Purwanto, Setyo; Heru, Bambang; Taufik, Agus
Jurnal Fisika dan Aplikasinya Vol 9, No 1 (2013)
Publisher : Jurnal Fisika dan Aplikasinya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (29.618 KB) | DOI: 10.12962/j24604682.v9i1.776

Abstract

Pengujian unjuk kerja sensor Giant Magnetoresistance (GMR) dilakukan dengan memberikan variasi medan elektromagnetik dan mengukur perubahan resistansi sensor sebagai tanggapan dari masukan. Program ini, yang dibuat dengan menggunakan bahasa Lab View, dapat digunakan untuk merancang rangkaian magnetik sebagai pembangkit medan elektromagnet. Parameter masukan untuk program ini antara lain permeability relatif inti, jumlah lilitan, dimensi inti, fringing dan panjang air gap. Sedangkan parameter keluaran yang dihasilkan adalah reluktansi inti dan air gap, fluks pada inti dan udara serta arus listrik yang dibutuhkan. Parameter masukan dihitung dengan menggunakan persamaan-persamaan medan elektromagnetik untuk mendapatkan keluaran. Dengan menggunakan program ini, pembuatan rangkaian magnetik sebagai sumber medan elektromagnet untuk pengujian sensor GMR dan penentuan parameter-parameter fisis yang akurat menjadi lebih mudah.  
Sistem Informasi Quality Assurance Proses Produksi Menggunakan Metode Agile Berbasis Web Ernawati, Ayu; Kurnia, Dadang; Hindasyah, Achmad
Jurnal Informatika Universitas Pamulang Vol 6, No 3 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i3.10272

Abstract

The company still uses manual recording of reports, namely using paper media in production activities, so during this pandemic it is quite difficult for admins and superiors to check reports. In addition, because the reporting process is still manual, data loss or data redundancy often occurs. This research designs and creates a web-based system to assist admins and superiors in checking reports. This system can provide item status reports, good item reports, service items, and damaged items, as well as monthly sales charts. For making this application using the agile method, where the application is made by collecting data which is then made an application that goes through the phases of planning, implementation, software testing, documentation, testing and maintenance. Based on the questionnaire on the satisfaction of using the system, a minimum number of 83.33 was obtained which indicates the application is in accordance with the company's request, where data can be stored properly and can detect double input data.
Analisis dan Implementasi Honeypot Honeyd Sebagai Low Interaction Terhadap Serangan Distributed Denial Of Service (DDOS) dan Malware Ubaidillah, Ubaidillah; Taryo, Taswanda; Hindasyah, Achmad
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 3 (2023): November
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i3.405

Abstract

Every computer device connected to a wide computer network is vulnerable to security risks. These threats encompass vulnerabilities to data, information, resources, and services within the system. These threats include intrusion, eavesdropping, theft of vital data, as well as damage to the network system. These actions are carried out by parties who are not accountable, commonly referred to as intruders or attackers. One method to prevent or anticipate these malicious actions is by utilizing the honeyd Honeypot technique. The honeyd Honeypot adopts a low-interaction approach, which involves indirect interaction with attackers. This Honeypot serves as a decoy or simulated server intentionally presented as a target for attacks. The purpose of this Honeypot is to detect and analyze ongoing attacks. In this research, the honeyd Honeypot is implemented as a simulated server resembling an authentic server. This server provides various services and opens several ports deliberately prepared as attack targets, such as Port 139, and Port 21.The results of this research unveil the existence of attacks. Signs of these attacks include a surge in network traffic, reaching up to 100 Megabits above the normal level. Another indicator is a sudden spike in CPU usage, reaching 100%. The activities of these attacks can be analyzed through the installed Wireshark application on the Honeypot server. Information obtained from this analysis encompasses details about the attacker's activities, enabling more effective preventive, anticipatory, and corrective measures. These steps encompass securing the server, network system, and existing services.
Comparative Analysis of Numerical Integration Solutions Pias Method and Newton Cotes Method Using Python Programming Language Rahayu, Santi; Hindasyah, Achmad
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 8 No. 4 (2023): Mathline: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v8i4.492

Abstract

Irregular areas cannot be solved by ordinary calculus formulas, so it is necessary to use numerical methods such as the Quadrature and Newton-Cotes methods. This research compares numerical integration solutions using the Quadrature method (Rectangular and Trapezoidal) and the Newton-Cotes method (Trapezoidal, Simpson 1/3, Simpson 3/8, and Weddle) with the Python programming language. Manual calculation of the first case study on integrals where the smallest error from the numerical method to the analytical method is achieved by the rectangular method of 0,017. In the second case study of tabular data for manual calculations the author only uses the Simpson 3/8 method with an absolute error for an analytical area of 1.418,583 km2. Whereas in the Python application for the first case study the smallest error was achieved by the Simpson 1/3 and Simpson 3/8 methods with an error of 0, in the sense that these two methods are very accurate to the actual analysis results. In the second case study the smallest error was achieved by the Simpson 1/3 method of 1.039,365 km2. The difference between manual calculations and application results is due to decimal rounding, and the linspace(a,b,n+1) function in the numpy library.
Analisis Pemetaan Kemampuan Akademik Mahasiswa Program Studi Teknik Informatika Dengan Metode Naive Bayes Classifier Dan K-Nearest Neighbor (KNN) (Studi Kasus: Universitas Pamulang) Sekar Putri, Utari; Taryo, Taswanda; Hindasyah, Achmad
Jurnal Sains dan Teknologi Vol. 7 No. 1 (2025): Jurnal Sains dan Teknologi
Publisher : CV. Utility Project Solution

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

Abstract

Pendidikan tinggi memiliki peran strategis dalam menghasilkan sumber daya manusia yang berkualitas, khususnya di bidang Teknik Informatika yang terus berkembang pesat. Penilaian performa akademik mahasiswa menjadi aspek penting untuk memastikan efektivitas pendidikan dan kualitas lulusan yang dihasilkan. Penelitian ini bertujuan untuk mengevaluasi proses pembelajaran mahasiswa Teknik Informatika dengan menerapkan teknik data mining, yaitu algoritma Naive Bayes Classifier (NBC) dan K-Nearest Neighbors (KNN). Metode penelitian yang digunakan melibatkan analisis data akademik mahasiswa Universitas Pamulang, yang mencakup tingkat keaktifan mahasiswa, pola kelulusan, serta faktor-faktor lain yang memengaruhi performa akademik. Proses analisis data dilakukan dengan membandingkan performa algoritma NBC dan KNN dalam memprediksi keberhasilan akademik mahasiswa berdasarkan parameter yang telah ditentukan. Selain itu, penelitian ini juga dilengkapi dengan survei yang bertujuan mengidentifikasi kendala yang memengaruhi tingkat kelulusan mahasiswa. Hasil penelitian menunjukkan bahwa metode KNN memiliki keunggulan dibandingkan NBC dengan akurasi masing-masing sebesar 91% untuk KNN dan 73% untuk NBC. Analisis data juga mengungkap adanya tren penurunan tingkat keaktifan mahasiswa dari 100% pada semester pertama menjadi 76,36% pada semester keempat, serta tingkat kelulusan tepat waktu yang sangat rendah, hanya 7,27% dari total populasi mahasiswa. Berdasarkan survei, kendala utama yang menyebabkan rendahnya tingkat kelulusan tepat waktu adalah masalah manajemen waktu, yang diakui oleh 71,43% responden, sementara beban tugas atau ujian tidak dianggap signifikan. Temuan ini menjadi dasar penting bagi pengelola program studi dalam merancang strategi pendidikan yang lebih responsif dan efektif untuk meningkatkan efisiensi studi serta kesiapan mahasiswa menghadapi tantangan dunia profesional.
SENTIMENT ANALYSIS OF PLN MOBILE APPLICATION SERVICES USING NAIVE BAYES, SUPPORT VECTOR MACHINE (SVM) AND DECISION TREE METHODS Prabowo, Bagus Adi; Hindasyah, Achmad; Khalid Rivai, Abu
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.378

Abstract

The advancement of information technology has driven public service providers such as PLN to introduce digital innovations, one of which is the PLN Mobile application that enables customers to access various services online. As the number of users increases, numerous reviews have been submitted through the Google Play Store platform, which can be utilized to evaluate service quality. This study aims to conduct sentiment analysis on user reviews of the PLN Mobile application using three classification algorithms: Naïve Bayes, Support Vector Machine (SVM), and Decision Tree. A total of 4,992 review data were collected and processed through text preprocessing stages, including case folding, tokenization, stopword removal, stemming, and vectorization using the TF-IDF method. The data were then split into training and testing sets with a ratio of 80:20 and trained using the three classification algorithms. Model evaluation was conducted using precision, recall, f1-score, and accuracy metrics. The evaluation results indicate that the SVM algorithm delivers the best performance with an accuracy of 94%, followed by Naïve Bayes and Decision Tree, each with an accuracy of 91%. However, all three models demonstrated limited effectiveness in detecting neutral sentiments. Based on these findings, the SVM algorithm is recommended as the most effective model for sentiment classification of PLN Mobile application reviews.
Co-Authors Agus Taufik, Agus Ayu Ernawati, Ayu Bambang Heru Dadang Kurnia, Dadang Khalid Rivai, Abu Prabowo, Bagus Adi Santi Rahayu Sekar Putri, Utari Setyo Purwanto Taswanda Taryo Ubaidillah Ubaidillah