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All Journal Indonesian Journal on Software Engineering (IJSE) Jurnal Teknologi Terpadu Jurnal Teknisi Journal of Computer Science and Informatics Engineering Jurnal Pengabdian Kepada Masyarakat Indonesia
Tresi Aprilia
Universitas Selamat Sri
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Engineering Public Health Social Sciences

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Implementasi Software Requirement Spesification dan Waterfall Model pada SIPODANG berbasis Android Mursalim Mursalim; Tresi Aprilia; Muhammad Achsin Samas
Indonesian Journal on Software Engineering (IJSE) Vol 10, No 1 (2024): IJSE 2024 (ON PROGRESS)
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijse.v10i1.21875

Abstract

Software Requirement Spesification atau SRS yang dipublikasikan oleh Institut of Electrical and Electronic Enginners (IEEE) digunakan untuk mempermudah dalam melakukan pengembangan sebuah sistem. SRS ada 3 tahapan besar yang dilakukan yaitu: introduction, overview description, spesification requirement. Dari ketiga tahapan tersebut terdapat sub tahapan diantaranya adalah menentukan tujuan dan cakupan dari sistem tersebut, mendeskripsikan secara umum tentang sistem tersebut, menganalisa kebutuhan fungsional, kebutuhan tampilan, kebutuhan performa sistem, desain, parameter pendukung, serta lampiran lain yang dibutuhkan selama melakukan pengembangan sistem. Sedangkan model Waterfall secara garis besar ada 4 tahapan yaitu: Perencanaan, Analisis, Desain dan Implementasi. Penelitian ini melakukan penggabungan antara SRS dan model waterfall untuk pengembangan aplikasi pengaduan masyarakat  di wilayah Kabupaten Batang. Hasil penelitian menunjukkan bahwa kombinasi SRS dengan model waterfall memberikan kemudahan dalam menginventarisasi kebutuhan sistem pada pengembangan sebuah perangkat lunak dan sistem yang dikembangkan dapat digunakan dengan baik untuk pengaduan masyarakat di wilayah Kabupaten Batang.
Peningkatan Kompetensi Digital Siswa melalui Pelatihan Pemrograman Web dan Pengolahan Data Taufik Hidayat; Yuni Handayani; Dian Novitaningrum; Tresi Aprilia; Tri Muji Waluyo; Derry Setiawan; To Adn Siroj
Jurnal Pengabdian kepada Masyarakat Indonesia (JPKMI) Vol. 5 No. 2 (2025): Agustus: Jurnal Pengabdian Kepada Masyarakat Indonesia (JPKMI)
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jpkmi.v5i2.7833

Abstract

Rendahnya literasi digital dan keterbatasan akses pembelajaran teknologi di lingkungan sekolah menjadi tantangan dalam perkembangan siswa menghadapi era digital. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kompetensi digital siswa MA NU 06 Cepiring melalui pelatihan pemrograman web dasar dan pengolahan data. Metode yang digunakan adalah pendekatan partisipatif dengan keterlibatan aktif siswa dalam seluruh tahapan pelatihan, mulai dari perencanaan, pelaksanaan, hingga evaluasi. Pelatihan dilaksanakan melalui beberapa sesi praktik langsung dengan materi HTML, CSS, penggunaan spreadsheet, dan pengenalan konsep basis data sederhana. Hasil menunjukkan bahwa siswa mengalami peningkatan kemampuan dalam memahami struktur pemrograman web dan keterampilan mengolah data. Selain itu, terlihat pula munculnya perilaku belajar mandiri, peningkatan kepercayaan diri dalam menggunakan teknologi, serta tumbuhnya kesadaran akan pentingnya kompetensi digital untuk masa depan. Implikasi dari kegiatan ini menunjukkan bahwa pendekatan pelatihan yang kontekstual dan kolaboratif mampu mendorong transformasi sosial di lingkungan sekolah, serta dapat dijadikan model replikasi untuk penguatan literasi digital di lembaga pendidikan lainnya.
Sistem Pemesanan Berbasis Laravel Livewire dengan WhatsApp Gateway di Perumahan Bumi Coprayan Indah Pekalongan Yoga Bagas Kurniawan; Tresi Aprilia; Muhammad Khozin; M. Achsin Samas
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2562

Abstract

The housing unit reservation system is still manual, with prospective buyers making reservations directly at the location offline, which often causes inefficiency, recording errors, and delays in information to prospective buyers. Therefore, this study aims to develop a Laravel and Livewire-based Housing Reservation Management System with a WhatsApp Gateway for Bumi Coprayan Indah Housing. This system is designed to enable prospective buyers to make housing unit reservations online, manage reservation data more efficiently, and receive automatic notifications via WhatsApp. The research method used is the Waterfall-based software development method, with the Laravel 10 framework, and the stages are requirements analysis, system design, implementation, and testing using the Black-Box Testing method and User Acceptance Testing (UAT). The test results indicate that the developed system functions well according to the predefined specifications. Based on these results, user satisfaction ranges from 76% to 80.4%, with the highest score for the house search feature (80.4%) and the lowest for the efficiency of the booking process (76%). This indicates that the system has been well accepted by users. The implementation of this system has a positive impact on reservation efficiency, reduces recording errors, and facilitates communication between property developers and prospective buyers. This system is expected to be an innovative solution for digitizing the housing reservation process in a more modern and structured manner.
Analysis Of The Convolutional Neural Network Method With Mobilenet Architecture In A Computer Vision-Based Industrial Waste Detection System Tresi Aprilia; Harlinda Rasvi Nabela; Beta Arya Ash Shidik
JURNAL TEKNISI Vol. 6 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/teknisi.v6i1.5559

Abstract

Abstract: Industrial waste has had a negative impact on the environment and human health. Industrial waste pollution can occur due to improperly managed waste disposal. Managing industrial waste can be one way to reduce the impact of environmental pollution. Checking industrial waste can be a solution for implementing oversight of waste management, but it will be difficult to implement if done in a large industrial area. Waste classification based on organic and non-organic waste categories. Convolutional Neural Networks (CNNs) and MobileNet can be applied to automatically detect organic and non-organic industrial waste systems. Researchers have conducted a comparative study between the Convolutional Neural Network (CNN) and MobileNet models, which is useful for obtaining the best model. The results of the analysis concluded that MobileNet has better accuracy, precision, and recall compared to the CNN model. The accuracy, precision, and recall generated by MobileNet are 99.5%, 99.4%, and 100%. Therefore, MobileNet is very suitable for implementation in an automatic industrial waste detection system in real-time applications.   Keyword: computer vision;  convolutional neural network (CNN); deep learning; industrial waste; mobilenet.  Abstrak: Limbah industri telah mengakibatkan dampak buruk bagi lingkungan dan kesehatan manusia. Pencemaran limbah industri dapat terjadi akibat pembuangan limbah yang tidak terkelola dengan baik. Pengelolaan limbah industri dapat menjadi salah satu cara untuk mengurangi dampak pencemaran lingkungan. Pengecekan limbah industri dapat menjadi solusi untuk menerapkan pengawasan terhadap pengelolaan limbah, namun akan sulit diterapkan jika dilakukan pada area industri yang luas. Klasifikasi limbah berdasarkan kategori limbah organik dan limbah non organik. Convolutional Neural Network (CNN) dan MobileNet dapat diterapkan untuk sistem pendeteksi limbah industri organik dan non organik secara otomatis. Peneliti telah melakukan studi komparatif antara model Convolutional Neural Network (CNN) dan MobileNet yang berguna untuk memperoleh model terbaik. Hasil analisa yang telah dilakukan menyimpulkan bahwasanya MobileNet mempunyai akurasi, precision dan recall yang lebih baik jika dibandingkan dengan model CNN. Akurasi, precision dan recall yang dihasilkan oleh MobileNet sebesar 99,5% 99,4% dan 100%. Oleh karena itu, MobileNet sangat cocok untuk diterapkan pada sistem deteksi limbah industri secara otomatis pada aplikasi real-time.Kata kunci: computer vision; convolutional neural network (CNN); limbah industri; mobilenet; pembelajaran mendalam.
Mapping Leading Commodities of Community Forest Plantations Based on Productivity Using the K-Means Clustering Algorithm Taufik Hidayat; Yuni Handayani; Muhammad Khozin; Tri Muji Waluyo; Dian Novitaningrum; Tresi Aprilia; Muchamad Achsin Samas
Journal of Computer Science and Informatics Engineering Vol 5 No 3 (2026): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i3.1797

Abstract

Kendal Regency has significant potential in community forest plantations, which contribute to the regional economy. However, the mapping of leading commodities based on productivity has not been conducted optimally. This study aims to map leading community forest plantation commodities using the K-Means Clustering algorithm. The novelty of this study lies in the application of the K-Means Clustering algorithm by integrating land area and production volume as the basis for mapping leading commodities at the regency level. Secondary data from the Central Bureau of Statistics of Kendal Regency for the 2019–2023 period, covering seven community forest plantation commodities, were used. The research stages included data preprocessing using Min-Max normalization, clustering into three clusters using the K-Means algorithm, and cluster evaluation employing the Within-Cluster Sum of Squares (WCSS). The results show that the K-Means algorithm successfully grouped the commodities into three clusters based on their productivity characteristics. Sugarcane formed a distinct cluster as the leading commodity due to its highest productivity despite its relatively small cultivation area. These findings provide data-driven insights to support decision-making for the development of community forest plantations in Kendal Regency
Co-Authors Beta Arya Ash Shidik Derry Setiawan Dian Novitaningrum Harlinda Rasvi Nabela M. Achsin Samas Muchamad Achsin Samas Muhammad Achsin Samas Muhammad Khozin Mursalim Mursalim Taufik hidayat To Adn Siroj Tri Muji Waluyo Yoga Bagas Kurniawan Yuni Handayani