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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) JNANALOKA Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Prosiding Seminar Nasional Teknologi Informasi dan Bisnis
Anjar Setiawan
Universitas Pamulang PSDKU Serang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Physics Social Sciences Transportation Other

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SISTEM PAKAR DIAGNOSA PENYAKIT INFEKSIUS HEWAN TERNAK SAPI Anjar Setiawan; Vihi Atina; Dwi Hartanti
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2022
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (436.032 KB)

Abstract

Abstrak—Biasanya peternak menanganinya dengan membuatjamu tradisional berupa campuran telur ayam jawa, madu, tempeyang setengah berjamur lalu digoreng dan dicampur jadi satu dandiberikan ke sapi tersebut. Setelah ditunggu dua sampai tiga jammasih belum ada perubahan peternak langsung menghubungimantri atau pakar terdekat untuk memberikan solusi awal bagimasalah peternak di daerah Boyolali terutama infeksius ternak sapi yaitu sapi helminthiasis (cacingan) yang diare terus-menerus, keluar cacing dari lubang anus, dan nafsu makan berkurang. Extreme Program merupakan metodologi dalampengembangan agile software development metodologis yangberfokus pada pengkodean (coding) yang menjadi aktivitasutama dalam semua tahapan pada siklus pengembanganperangkat lunak. Dengan 4 metode Extreme Programming yangdilakukan yaitu planning, design, coding, dan testing. MetodeCF digunakan dalam penerapan sistem pakar ini untuk mengukurtingkat kepastian dalam mendiagnosis penyakit. Sistem pakaryang dihasilkan terdapat dua hak akses yaitu pakar danpengguna. Fitur hak akses pakar yaitu admin, penyakit, gejala,pengetahuan, post keterangan, ubah password, dan tentang.Kemudian hak akses pengguna yaitu keterangan penyakit,diagnosa penyakit, riwayat penyakit, info harga dan tentang padamenu beranda. Kesimpulan testing (pengujian) berhasilmenerapkan pengujian sistem menggunkan metode black boxtesting, pengujian perhitungan, dan pengujian kuesioner.Pengujian kuesioner untuk pakar memiliki nilai rata-rata totalpresentase setuju 100% dan untuk pengguna memiliki rata-ratatotal presentase sangat setuju 25% dan setuju 75%.
Requirements Engineering untuk Pengembangan Aplikasi Pemesanan Hotel Setiawan, Anjar
JNANALOKA Vol. 06 No. 01 Maret Tahun 2025
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2024.v5-no02-%p

Abstract

This research describes a Requirements Engineering approach focused on stakeholder development in the context of a hotel booking platform. By looking at various stakeholders such as hotels, customers, catering service providers, photographers, and cosmetologists, this research aims to detail the unique needs of each party involved. Stakeholder identification is the first step, followed by an in-depth analysis of the needs of each stakeholder. The main focus includes platform functionality, payment integration, order management, and stakeholder interaction. Data security, privacy, thorough testing, and long-term maintenance planning ensure successful implementation. The results of this research can provide practical guidance for developers in designing a hotel booking platform that meets room reservation standards and integrates additional services optimally, meeting the needs and expectations of each stakeholder involved.
Cattle Weight Estimation Using Linear Regression and Random Forest Regressor Anjar Setiawan; Ema Utami; Dhani Ariatmanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5494

Abstract

The global cattle farming industry has benefits as a food source, livelihood, economic contribution, land environmental restoration, and energy source. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. The authors propose estimating cattle weighting linear regression and random forest regression. Linear regression can interpret the linear relationship between dependent and independent variables, and random forest regression can generalize the data well. The data set used in this study consisted of ten variables: live body weight, withers height, sacrum height, chest depth, chest width, maclocks width, hip joint width, oblique body length, oblique back length and chest circumference. Find the model that produces the smallest MAE value. The results show that the linear regression algorithm can produce estimated weight values for cattle with the best performance. This model produces a mean absolute error (MAE) of 0.35 kg, a mean absolute percentage error (MAPE) of 0.07%, a root mean square error (RMSE) of 0.5 kg, and an R² of 0.99. Each variable has excellent correlation performance results and contributes to computer vision and machine learning.
Sistem Keamanan Rumah Pintar Berbasis Sensor ESP32-Cam dan PIR Dengan Notifikasi Teknologi Bot Whatsapp Hamuda, Hayadi; Setiawan, Anjar
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3387

Abstract

A smart home is a system that basically consists of intelligent elements that are interconnected and integrated with each other through the use of internet networks based on the Internet of Things. Today, smart home technology has been utilised in various rooms in contemporary homes. Several components, such as the ESP32-Cam microcontroller, of these smart home devices are installed in the room and include a motion sensor or PIR (Passive Infrared Receiver), buzzer, and WhatsApp notification software. When motion or activity is detected in the room, the components connected and integrated with the internet network will send notifications to a laptop or WhatsApp messaging programme in the form of text and photos. The results of tool testing and overall system testing data show that the PIR sensor can detect motion at a distance of 1 to 3 metres marked by the activation of the buzzer and the appearance of WhatsApp messages with an average delay of 1 to 3 seconds. Experiments were also carried out based on the length of the 5-pin USB cable, and the results showed that the length of the cable affected the delay in sending WhatsApp notifications in addition to wifi or internet connection. WhatsApp notifications take longer to send the longer the cable is. By using this smart home appliance, home dependability and security can be improved. Keywords: ESP32-Cam, PIR Sensor, Smart Home, Whatsapp
Hybrid Fuzzy Logic, Genetic Algorithms, and Artificial Neural Networks for Cattle Body Weight Prediction Anjar Setiawan; Ema Utami; Dhani Ariatmanto
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i2.1319

Abstract

Cattle serve as the primary means of meat and milk production in numerous regions across the globe. Enhancing efficiency and productivity in cow ranching can provide significant economic consequences. The cattle industry is significant as it enables the estimation of cow weight, directly influencing beef and milk quality. This study aims to enhance the accuracy of cattle weight estimation by minimizing the Mean Squared Error (MSE) values. The integration of artificial neural network (ANN), fuzzy logic (FL), and genetic algorithm (GA) techniques is a promising artificial intelligence tool for predicting and modeling cattle weight in livestock weight prediction systems. The cow weight forecast yielded a Mean Squared Error (MSE) value of 10.9 kg, which is the best result. The results demonstrate the progress made in agriculture using advanced technologies. They offer a detailed examination of how artificial intelligence, fuzzy logic, and evolutionary techniques can be combined to address the many difficulties associated with estimating cattle body weight.
Pengembangan Sistem Informasi Pembelajaran Interaktif Berbasis Cloud Untuk Meningkatkan Partisipasi Siswa Di SMKN 7 Kota Serang Anjar Setiawan; Angga Pramadjaya; Dimas Permana; Dyas Rasyid; Evan Musa Pratama; Fikri Maulana Iqbal; Zoe Zia Sidan
Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat Vol 6 No 1 (2025): Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/abdilaksana.v6i1.47057

Abstract

Peningkatan partisipasi siswa dalam pembelajaran merupakan salah satu tantangan utama di era digital, terutama di SMKN 7 Kota Serang, di mana tingkat keterlibatan siswa dalam proses belajar mengajar masih rendah. Penelitian ini bertujuan mengembangkan Sistem Informasi Pembelajaran Interaktif berbasis Cloud yang dirancang untuk menyediakan platform pembelajaran yang fleksibel, inovatif, dan kolaboratif bagi siswa dan guru. Sistem ini mencakup fitur utama seperti pengelolaan materi digital berbasis cloud, forum diskusi daring untuk meningkatkan interaksi, kuis daring sebagai evaluasi pembelajaran, serta pengelolaan tugas yang dapat diakses secara real-time. Metodologi penelitian menggunakan pendekatan System Development Life Cycle (SDLC) dengan tahapan yang mencakup analisis kebutuhan pengguna melalui wawancara dan survei, perancangan sistem menggunakan diagram alur data (DFD) dan prototipe antarmuka, implementasi berbasis platform cloud seperti Google Firebase, hingga evaluasi efektivitas sistem menggunakan survei dan pengukuran data kuantitatif. Hasil evaluasi menunjukkan bahwa sistem ini meningkatkan partisipasi siswa sebesar 40% dibandingkan metode konvensional. Peningkatan ini dicapai melalui penyediaan akses fleksibel terhadap materi pembelajaran, yang memungkinkan siswa belajar kapan saja dan di mana saja menggunakan perangkat yang tersedia. Selain itu, fitur interaktif seperti forum diskusi dan kuis daring meningkatkan motivasi siswa untuk terlibat aktif dalam proses pembelajaran.
Rekayasa kebutuhan untuk pengembangan sistem perangkat lunak pelayanan kesehatan: Literatur Reviu Sistematis Setiawan, Anjar; Auliyah, Ulul Azmiati; Noviyanto, Noviyanto; Basit, Muhammad Abdul; Sidiq, Muhammad
JNANALOKA Vol. 05 No. 01 Maret Tahun 2024
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2024.v5-no01-1-11

Abstract

Penelitian ini dilakukan untuk memberikan refleksi sistematis terhadap pengembangan sistem perangkat lunak untuk pelayanan kesehatan dengan fokus pada kebutuhan rekayasa. Hal ini dilakukan karena sistem perangkat lunak untuk pelayanan kesehatan merupakan sektor yang sangat kompleks dan dinamis. Proses ini melibatkan elisitasi, analisis, spesifikasi, validasi, dan manajemen persyaratan. Penelitian ini bertujuan untuk memberikan wawasan sistematis mengenai fase, teknik, dan alat yang digunakan dalam pengembangan persyaratan sistem perangkat lunak untuk layanan kesehatan, serta untuk mendiseminasikan kualitas penelitian yang ada. Beberapa teknik dapat digunakan dalam rekayasa persyaratan untuk mengembangkan sistem perangkat lunak untuk layanan kesehatan, seperti survei, wawancara, kasus penggunaan UML, dan prototipe. Perkembangan sistem kebutuhan perangkat lunak pada pelayanan kesehatan masih memberikan peluang yang sangat besar untuk pengembangan lebih lanjut khususnya sistem pelayanan kesehatan pada topik pencatatan data kesehatan secara digital yang menjadi isu terkini dan sangat dibutuhkan oleh masyarakat.
CATTLE BODY WEIGHT PREDICTION USING REGRESSION MACHINE LEARNING Anjar Setiawan; Utami, Ema; Ariatmanto, Dhani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1521

Abstract

Increasing efficiency and productivity in the cattle farming industry can have a significant economic impact. Cow health and productivity problems directly impact the quality of the meat and milk produced. In the cattle farming industry, it can help predict cow weight oriented to beef and milk quality. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. This research aims to predict cow weight by increasing the results of smaller MAE values. The methods used are linear Regressor (LR), Random Forest Regressor (RFR), Support Vector Regressor (SVR), K-Neighbors Regressor (KNR), Multi-layer Perceptron Regressor (MLPR), Gradient Boosting Regressor (GBR), Light Gradient boosting (LGB), and extreme gradient boosting regressor (XGBR). Producing cattle weight predictions using the SVR method produces the best values, namely mean absolute error (MAE) of 0.09 kg, mean absolute perception error (MAPE) of 0.02%, root mean square error (RMSE) of 0.08 kg, and R-square of 0.97 compared to with other algorithm methods and the results of statistical correlation analysis showed several significant relationships between morphometric variables and live weight.
Co-Authors Ahmad Habibi Maftukhan Angga Pramadjaya Atina, Vihi Auliyah, Ulul Azmiati Basit, Muhammad Abdul DHANI ARIATMANTO Dimas Permana Dyas Rasyid Ema Utami Evan Musa Pratama Fikri Maulana Iqbal Hamuda, Hayadi Hartanti, Dwi Muhamad Abdul Aziz Muhammad Sidiq Noviyanto Noviyanto, Noviyanto Zoe Zia Sidan