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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTI Nusa Mandiri Journal Peqguruang: Conference Series Journal Online Manajemen ELPEI (JOMEL) semanTIK
Imran Taufik
Universitas Handayani Makassar
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Pengaruh Sharing Pengetahuan Budidaya Rumput Laut Terhadap Pendapatan Petani Rumput Laut di Dusun Tolebeng Kecamatan Penrang Kabupaten Wajo Nashriah Akil; Misbahuddin; Ihsan Guntur; Imran Taufik; Abdullah
Jurnal Online Manajemen ELPEI Vol 3 No 2 (2023)
Publisher : STIM-LPI Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58191/jomel.v3i2.149

Abstract

This research was conducted in Wajo district using knowledge sharing as an effort to increase income for seaweed farming farmers, while the sample in this study was 39 people as objects as well as being the population of Tolebeng village, after testing the hypothesis and accepting it, it was continued with validity and reliability.The results of this study indicate that knowledge sharing has an effect on the level of income of the Tolebeng village community which has a significance below 0.5, which means that knowledge sharing has an effect on the income of the Tolebeng community.
Analisis Ketersediaan Stok Mata Uang Asing Terhadap Volume Kebutuhan Konsumen Menggunakan Metode K-Means: Studi Kasus : PT. Haji La Tunrung AMC Kota Makassar Muhammad Fuad; Mashur Razak; Imran Taufik
SemanTIK : Teknik Informasi Vol. 11 No. 1 (2025): Vol. 11 No. 1 (2025): SemanTIK Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v11i1.104

Abstract

Penelitian ini bertujuan untuk menganalisis ketersediaan stok mata uang asing pada cabang PT. Haji La Tunrung AMC terhadap volume kebutuhan konsumen menggunakan metode K-Means. Penelitian didasarkan pada banyaknya kebutuhan mata uang asing untuk penukaran sehingga ada kelebihan dan kekurangan stok mata uang asing yang mengakibatkan penumpukan stok. Diperlukannya analisis data besar atas  ketersedian stok mata uang asing secara cepat dan akurat dalam memenuhi permintaan mata uang asing yang dibutuhkan. Peneliti menggunakan data primer dari PT Haji La Tunrung AMC periode 2019-2023  dengan akumulasi data terhadap penjualan 352.990 mata uang asing dan data sekunder dari literatur.  Terdapat hasil 5 klaster adalah yang terbaik berdasarkan Elbow Method dengan DBI terendah (0.2922) dan Silhouette Score tinggi (0.7913). Hasil ini menunjukan juga mata uang mana saja yang direkomendasi dengan skala preoritas sesuai kebutuhan stok, Cluster 0: "Kategori Tinggi" Cluster 1: "Kategori Menengah Tinggi" Cluster 2: "Kategori Menengah" Cluster 3: "Kategori Menengah Rendah" Cluster 4: "Kategori Rendah". This study aims to analyze the Availability of Foreign Currency Stock at PT. Haji La Tunrung AMC Branch Against the Volume of Consumer Needs Using the K-Means Method. The study is based on the large amount of foreign currency in circulation for exchange so that there is an excess and shortage of foreign currency stock which results in accumulation. It is necessary to analyze big data on the availability of Foreign Currency Stock quickly and accurately in meeting the demand for foreign currency needed. The researcher used primary data from PT Haji La Tunrung AMC for the period 2019-2023 with accumulated data on sales of 352,990 and secondary data from the literature. 5 clusters are the best based on the Elbow Method with the lowest DBI (0.2922) and high Silhouette Score (0.7913). These results also show which currencies are recommended with a priority scale according to stock needs, Cluster 0: "High Category" Cluster 1: "High Medium Category" Cluster 2: "Middle Category" Cluster 3: "Low Medium Category" Cluster 4: "Low Category".
Analisis Tren Penelitian Neuro Linguistic Programming Menggunakan Pendekatan Automatic Text Annotation Muslihan Ian; Supriadi Sahibu; Imran Taufik
Journal Peqguruang: Conference Series Vol 6, No 1 (2024): Peqguruang, Volume 6 Nomor 1 Mei 2024
Publisher : Universitas Al Asyariah Mandar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35329/jp.v5i2.4857

Abstract

Analisis tren penelitian merupakan jenis pengolahan bahasa alami untuk menarik suatu kata kunci dari penelitian mahasiswa tentang topik tertentu. Analisis tren penelitian melibatkan dalam membangun sebuah sistem untuk mengumpulkan dan memeriksa topik penelitian mahasiswa yang dibuat dalam repository perpustakaan atau jurnal pada suatu lembaga Pendidikan.Tujuan dari penelitian ini adalah untuk menentukan tren penelitian pada jurnal penelitian suatu lembaga pendidikan, dengan menerapkan pendekatan Automatic Text Anotation dapat menfilter kalimat yang ingin ditemukan dalam suatu jurnal dengan cepat dan efisien.Hasil dari penelitian dari Penelitian ini Melalui penggunaan algoritma LSTM ini, berbagai publikasi dengan tema Sentimen Analisis berhasil dikumpulkan yaitu jurnal nasional dan jurnal internasional tahun tahun 2020-2022 dan diolah secara otomatis untuk mengidentifikasi tren dari penelitian seperti Focus, domain dan Technique yang digunakan dalam penelitian tersebut memperoleh hasil penelitian yakni, dengan menggunakan Skenario split dataset sebesar 90% data latih dan 10% data testing dengan epoch 200 maka didapatkan tingkat akurasi dari permodelan LSTM yaitu sebaesar 93.73%. dan diperoleh validasi akurasi 94.94% dan diperoleh nilai Loss sebesar 0.189% dan validasi Loss sebesar 0.195%. maka dapat disimpulkan bahwa model LSTM dapat melakukan prediksi atau Automatic Text Annotation dikarenakan memiliki akurasi sebesar 100% dari hasil pengujian algoritma.
Multimodal Sensor Evaluation for Fish Pond Water Quality Monitoring Zein Rifal; Syafruddin Syarif; Imran Taufik; Mashur Razak; Supriadi Sahibu; Respaty Namruddin
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12656

Abstract

Freshwater aquaculture requires continuous water quality monitoring because rapid changes in temperature, pH, dissolved oxygen, turbidity, total dissolved solids, and water level can affect fish health and pond productivity. This study evaluates a multimodal sensor system for real-time fish pond water quality monitoring and dashboard-based actuator control. The system integrates six sensors with Arduino Mega for signal acquisition, ESP32 for Wi-Fi communication, Firebase for cloud data storage and command exchange, and a Flutter dashboard for visualization and manual control. Field testing was conducted in two tilapia ponds with different initial conditions. Sensor performance was evaluated by comparing five measurable parameters with reference instruments using percentage error, accuracy, mean absolute error, and root mean square error, while turbidity was assessed through functional contrast testing and short-term stability because a turbidity reference instrument was unavailable. The average accuracy of the five validated parameters was 87.37% in pond 1 and 95.58% in pond 2. Temperature and water level showed the highest accuracy, above 98% in both ponds. Dissolved oxygen and total dissolved solids showed larger deviations, especially in pond 1, indicating sensitivity to field conditions and calibration stability. Actuator commands for the aerator and circulation pumps responded within 1-2 seconds under stable network conditions. The results show that the system is useful as a preliminary field-validated monitoring and semi-automatic control platform, but further work is required for long-term drift testing, turbidity validation using a commercial meter, and automatic control evaluation.
IMPLEMENTASI MODEL DeiT UNTUK MEMBEDAKAN GAMBAR BUATAN AI DAN MANUSIA PADA ILUSTRASI ANIMASI 2D Ibnu Taimiyah Erwin; Abdul Latief Arda; Imran Taufik; Muhammad Erwin Rosyadi. S; Hilyatul Auliyah Erwin
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6306

Abstract

The development of artificial intelligence (AI) has influenced various fields, including art and visual design. AI Generative Art, which mimics human styles, has sparked debates on originality, artistic value, as well as legal and ethical challenges. Therefore, methods are needed to distinguish between AI-generated and human-made images, particularly in 2D animation illustrations. This study proposes the use of Data-efficient Image Transformers (DeiT) for image classification. Two models tested are DeiT Base and DeiT Tiny, using a dataset of 6,000 images equally divided between AI and human categories. The dataset is split into training (70%), validation (15%), and testing (15%). Experimental results show that DeiT Base achieves over 95% accuracy with fast convergence and optimal loss function stability. Meanwhile, DeiT Tiny attains around 93% accuracy, being more computationally efficient despite requiring more epochs for stability. Compared to previous models using a larger dataset (11,000 images per category) but achieving only 80% accuracy, DeiT performs better in both accuracy and computational efficiency, even with a smaller dataset. In conclusion, DeiT is effective for classifying 2D animation images. DeiT Base excels in accuracy and convergence speed, while DeiT Tiny is more resource-efficient, making it an ideal choice for environments with computational constraints.
Co-Authors Abdul Latief Arda Abdullah Hilyatul Auliyah Erwin Ibnu Taimiyah Erwin Ihsan Guntur Mashur Razak Misbahuddin Muhammad Erwin Rosyadi. S Muhammad Fuad Muslihan, Muslihan Nashriah Akil Respaty Namruddin Supriadi Sahibu Syafruddin Syarif Zein Rifal