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All Journal BERKALA SAINSTEK Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Teknik Informatika (JUTIF) BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Kreativitas dan Inovasi (Jurnal Kreanova) Insand Comtech : Information Science and Computer Technology Journal Jurnal Pengabdian Masyarakat Journal of Digital Literacy and Volunteering Journal of Informatics Development semanTIK RECORD Journal of Loyality and Community Development Journal of Digital Literacy and Volunteering
Fauziah, Difari Afreyna
Institut Teknologi Dan Sains Mandala
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Penerapan Metode Analytical Hierarchy Process (AHP) Pada Penilaian Pegawai Teladan Muliawan, Agung; Sabilirrasyad, Iqbal; Fauziah, Difari Afreyna
Journal of Digital Literacy and Volunteering Vol. 2 No. 2 (2024): July
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v2i2.76

Abstract

One of the factors supporting the success of a business place is productive employees who have maintained and improved qualification standards. The company's appreciation for exemplary employees can be given by giving gifts or awards. Employee performance assessment can be done to determine employees who are qualified and highly dedicated to the company. However, many companies experience difficulties in evaluating employee performance because the calculations are still manual so that they are less effective and objective, one of which is SMK Visi Global Jember. The research will apply the Analytical Hierarchy Process (AHP) method in determining the best employees at SMK Visi Global Jember so that the selection process is right on target with the needs of the criteria given. The required criteria include honesty, loyalty, commitment, discipline and cooperation which will be processed to produce the highest rank for determining recommendations for exemplary employees. The results of this study produce a Consistency Ratio (CR) value of 0.083 so that the value of giving preferences is consistent and can be used in determining exemplary employees at SMK Visi Global Jember
PENDAMPINGAN MANAJEMEN USAHA GUNA MENINGKATKAN DAYA SAING SATWA SEHAT KARIMATA DI JEMBER Sulaksono, Hary; Hidayah, Tamriatin; Fauziah, Difari Afreyna
Jurnal Kreativitas dan Inovasi (Jurnal Kreanova) Vol 5 No 1 (2025): Januari
Publisher : Sekolah Tinggi Ilmu Ekonomi Indonesia (STIESIA) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24034/kreanova.v5i1.6941

Abstract

Pendampingan manajemen usaha praktis dan adaptif pada PKM Satwa Sehat ini bertujuan untuk meningkatkan kinerja Satwa Sehat Petshop Karimata di Jember, Jawa Timur, melalui pendekatan kolaboratif antara praktisi usaha dan akademisi. Analisis situasi menunjukkan adanya potensi pasar yang besar di Kota Jember, dengan tingginya minat masyarakat terhadap hewan peliharaan. Namun, mitra usaha menghadapi berbagai permasalahan, termasuk pengelolaan persediaan, manajemen keuangan, dan pemasaran yang belum optimal. Tim pengabdian Institut Teknologi dan Sains Mandala melakukan wawancara mendalam dengan mitra untuk mengidentifikasi masalah dan merumuskan solusi. Program pendampingan yang dirancang meliputi pengelolaan persediaan berbasis aplikasi, pelatihan manajemen keuangan, dan penguatan sumber daya manusia. Selain itu, pendekatan pemasaran digital juga diterapkan untuk meningkatkan brand awareness. Diharapkan, dengan implementasi solusi tersebut, Satwa Sehat Petshop Karimata dapat meningkatkan efisiensi operasional, memperkuat posisi pasar, dan mencapai keberlanjutan usaha yang lebih baik, sekaligus memberikan kontribusi positif terhadap kesejahteraan hewan peliharaan di masyarakat.
Penerapan Metode Analytical Hierarchy Process (AHP) Pada Penilaian Pegawai Teladan Muliawan, Agung; Sabilirrasyad, Iqbal; Fauziah, Difari Afreyna
Journal of Digital Literacy and Volunteering Vol. 2 No. 2 (2024): July
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v2i2.76

Abstract

One of the factors supporting the success of a business place is productive employees who have maintained and improved qualification standards. The company's appreciation for exemplary employees can be given by giving gifts or awards. Employee performance assessment can be done to determine employees who are qualified and highly dedicated to the company. However, many companies experience difficulties in evaluating employee performance because the calculations are still manual so that they are less effective and objective, one of which is SMK Visi Global Jember. The research will apply the Analytical Hierarchy Process (AHP) method in determining the best employees at SMK Visi Global Jember so that the selection process is right on target with the needs of the criteria given. The required criteria include honesty, loyalty, commitment, discipline and cooperation which will be processed to produce the highest rank for determining recommendations for exemplary employees. The results of this study produce a Consistency Ratio (CR) value of 0.083 so that the value of giving preferences is consistent and can be used in determining exemplary employees at SMK Visi Global Jember
Implementasi Artificial Intelligence untuk Prediksi Penyakit Jantung dengan Pendekatan Explainable AI Bima Wahyu Maulana; Agung Muliawan; Muhammad Hafidh Firmansyah; Angga Dwinanda; Difari Afreyna Fauziah
Insand Comtech : Information Science and Computer Technology Journal Vol. 11 No. 1 (2026): Insand Comtech
Publisher : Universitas Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53712/jic.v11i1.2972

Abstract

Fokus utama penelitian ini adalah rancang bangun sistem prediksi penyakit kardiovaskular dengan mengimplementasikan algoritma Random Forest. Guna mengatasi masalah "kotak hitam" pada kecerdasan buatan, studi ini mengintegrasikan pendekatan Explainable Artificial Intelligence (XAI) melalui metode SHAP (SHapley Additive exPlanations) untuk memperkuat aspek transparansi model. Analisis dilakukan menggunakan data sekunder dari Heart Disease UCI Dataset, yang mencakup variabel klinis esensial seperti usia, gender, tekanan darah, hingga kadar kolesterol. Siklus riset dimulai dari tahap pra-pemrosesan data, distribusi data latih dan uji, hingga fase konstruksi model. Evaluasi efektivitas sistem diukur menggunakan metrik akurasi, precision, recall, dan F1-score. Temuan empiris menunjukkan bahwa model mampu menghasilkan tingkat akurasi sebesar 83%, dengan kapabilitas klasifikasi yang stabil baik pada pasien positif maupun negatif penyakit jantung. Lebih lanjut, penerapan SHAP mengungkap bahwa variabel oldpeak, thalach, dan ca menjadi determinan paling krusial dalam pembentukan prediksi. Menariknya, fitur seperti kolesterol tidak selalu memberikan dampak linier langsung, melainkan bekerja melalui interaksi kompleks dengan atribut lainnya. Secara keseluruhan, integrasi XAI dalam studi ini terbukti meningkatkan interpretabilitas model machine learning. Hal ini diharapkan dapat memberikan landasan klinis yang lebih kuat bagi tenaga medis dalam proses pengambilan keputusan serta mempertebal kepercayaan terhadap sistem diagnostik berbasis AI.
Deteksi Deepfake dalam Mitigasi Risiko Disinformasi Digital menggunakan Metode Multi-Task Cascaded Convolutional Network dan Mobilenetv2 di Indonesia Helmi Agus Salim; Agung Muliawan; Difari Afreyna Fauziah
SemanTIK : Teknik Informasi Vol. 11 No. 2 (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.v11i2.239

Abstract

Disinformasi digital yang semakin marak melalui penyebaran konten deepfake telah menjadi tantangan serius di era transformasi digital, khususnya di Indonesia. Penelitian ini bertujuan untuk mengembangkan dan membandingkan kinerja dua metode deteksi deepfake berbasis deep learning, yaitu Multi-Task Cascaded Convolutional Network (MTCNN) dan MobileNetV2. Penelitian difokuskan pada deteksi manipulasi citra wajah dengan memanfaatkan kekuatan ekstraksi fitur dari MTCNN dan efisiensi arsitektur ringan MobileNetV2. Proses penelitian melibatkan tahapan pre-processing data menggunakan metode ekstraksi wajah, pelatihan model klasifikasi biner, serta evaluasi performa melalui confusion matrix. Hasil penelitian menunjukkan bahwa MTCNN menghasilkan performa klasifikasi yang lebih baik dibandingkan MobileNetV2, dengan akurasi sebesar 84% dan nilai True Positive serta True Negative yang lebih tinggi. Sebaliknya, MobileNetV2 hanya mampu mencapai akurasi 78,5%, disertai tingkat False Negative dan False Positive yang lebih besar. Temuan ini menunjukkan bahwa MTCNN memiliki keunggulan dalam mendeteksi konten deepfake secara lebih akurat berkat kemampuan ekstraksi fitur wajah yang lebih presisi. Di sisi lain, MobileNetV2 menawarkan kecepatan dan efisiensi pengolahan data, namun kurang optimal untuk mendeteksi manipulasi detail pada wajah. Berdasarkan hasil penelitian, disimpulkan bahwa MTCNN lebih direkomendasikan sebagai metode deteksi deepfake dalam upaya mitigasi risiko disinformasi digital di Indonesia, terutama pada skenario yang menuntut akurasi tinggi. Increasingly widespread digital disinformation through the spread of deepfake content has become a serious challenge in the era of digital transformation, especially in Indonesia. This research aims to develop and compare the performance of two deep learning-based deepfake detection methods, namely Multi-Task Cascaded Convolutional Net-work (MTCNN) and MobileNetV2. The research focused on facial im-age manipulation detection by utilizing the feature extraction power of MTCNN and the lightweight architectural efficiency of MobileNetV2. The research process involved data pre-processing using face extraction methods, binary classification model training, and performance evalua-tion through confusion matrix. The results showed that MTCNN pro-duced better classification performance than MobileNetV2, with 84% accuracy and higher True Positive and True Negative values. In con-trast, Mo-bileNetV2 was only able to achieve 78.5% accuracy, along with higher False Negative and False Positive rates. The findings show that MTCNN has the advantage of detecting deepfake content more accurately thanks to its more precise facial feature extraction capabili-ties. On the other hand, MobileNetV2 offers speed and efficiency in data processing, but is less optimum for detecting manipulation of faci-al details. Based on the results, it is concluded that MTCNN is more recommended as a deepfake detection method in an effort to mitigate the risk of digital disinformation in Indonesia, especially in scenarios that demand high accuracy
Co-Authors Abdul Wahid Achmad Maududie Agung Muliawan Ahmad Nurdianyah Aini, Arifatul Andita Prasetyo , Nur Andita Prasetyo, Nur Angga Dwinanda Angga Pranadaning, Fadila Arini Shufia Dwi Sukmawati Bima Wahyu Maulana Daffa Duta Perdana, Mohammad Dwi Natasya, Amelia Edwar Yudhistira, Gedion Hamzah Fansuri Hary Sulaksono Helmi Agus Salim Hermansyah, Masud Hermawan, Muhammad Ida Ayu Putu Sri Widnyani Ifrina Nuritha Iqbal Sabilirrasyad laili, Nufilatul Muhaimin Dimyati Muhamat Abdul Rohim MUHAMMAD HAFIDH FIRMANSYAH Narisa, Defani Nurfaizeh, Nurfaizeh Nurhayati Nurhayati Oktavinda, Diana Purnamie Titisari Shofyan, Firdaus Syaifuddin, Ageng Tamriatin Hidayah Wiranto, Ferry Zahro Alifia Rohma, Sofiatus