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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Simetris JOIV : International Journal on Informatics Visualization IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Teknik Komputer AMIK BSI Paradigma Journal of Information Technology and Computer Science (JOINTECS) Jurnal CoreIT Techno Nusa Mandiri : Journal of Computing and Information Technology Infoman's: Jurnal Ilmu-Ilmu Manajeman dan Informatika Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research INTI Nusa Mandiri Jurnal Sistem Informasi KLIK: Kajian Ilmiah Informatika dan Komputer Computer Science (CO-SCIENCE) Paradigma Jurnal Insan Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS SATIN - Sains dan Teknologi Informasi Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen
Yuris Alkhalifi
Universitas Bina Sarana Informatika, Jakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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IMPLEMENTASI ARTIFICIAL INTELLIGENCE PADA DESAIN GRAFIS DALAM MEMAKSIMALKAN PERAN MEDIA SOSIAL REMAJA BAITUL HALIM Yuris Alkhalifi; Khairul Rizal; Amir; Achmad Fachrurozi
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 2 No. 6 (2024): Desember
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v2i6.1481

Abstract

Perkembangan Artificial Intelligence (AI) mempermudah pembuatan konten visual yang kreatif dan efisien. Media sosial, sebagai platform utama remaja untuk berekspresi, mempromosikan bisnis, dan membangun jaringan, sering kali tidak dimanfaatkan optimal karena keterbatasan keterampilan desain grafis. Pengabdian ini bertujuan untuk menyelenggarakan pelatihan desain grafis berbasis AI bagi remaja Baitul Halim (RBH) untuk meningkatkan kreativitas mereka dalam pengelolaan konten media sosial. Pelatihan mencakup penggunaan alat AI seperti Canva dan ChatGPT untuk membuat desain otomatis, memanfaatkan template dinamis, serta strategi pemasaran konten yang relevan dengan tren terkini. Hasil kegiatan menunjukkan bahwa peningkatan signifikan dalam keterampilan desain peserta, yang mampu menciptakan konten menarik dan berkualitas tinggi. Selain itu, pelatihan ini memperluas wawasan mereka terhadap peluang karier dan wirausaha di bidang digital dan kreatif. Dengan itu, integrasi teknologi AI dalam desain grafis tidak hanya mempercepat proses produksi, tetapi juga mempersiapkan remaja untuk menghadapi dinamika teknologi dan media sosial di masa depan. Kata Kunci: Desain Grafis, Artificial Intelligence, Media Sosial, Kreativitas Remaja
Prediksi Stok Barang pada Klinik Kecantikan Nastyaderm Karawang Menggunakan Metode Trend Moment Puspita, Kartika; Alkhalifi, Yuris
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 15, No 2 (2024): JURNAL SIMETRIS VOLUME 15 NO 2 TAHUN 2024
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v15i2.12165

Abstract

Pengelolaan stok barang yang efisien merupakan aspek penting dalam mendukung kelancaran operasional dan kepuasan pelanggan. Stok barang yang berlebih dapat menyebabkan meningkatnya biaya operasional dan mengakibatkan kerugian akibat produk kadaluarsa, sementara jika kekurangan stok barang dapat menurunkan kualitas layanan dan kepuasan pelanggan. Oleh karena itu, memprediksi kebutuhan stok barang secara akurat menjadi tantangan dalam dunia bisnis saat ini termasuk pada sektor jasa seperti klinik kecantikan. Dalam memproyeksikan tren permintaaan dimasa depan dapat dianalisis dengan pola penjualan berdasarkan data dimasa lampau. Metode Trend Moment adalah salah satu metode yang dapat mengoptimalkan manajemen stok barang dengan membaca pola data penjualan masa lampau. Tujuan penelitian ini adalah memprediksi stok barang dimasa yang akan datang dengan menggunakan metode trend moment pada klinik kecantikan Nastyaderm Karawang. Data penjualan produk yang digunakan pada penelitian ini data penjualan klinik Kecantikan Nastyaderm Karawang selama 36 bulan terhitung dari bulan November 2021-Oktober 2024, dengan menggunakan 1 produk yang dijadikan data sampel yakni produk Serum Glowing. Kemudian dilakukan juga evaluasi dengan menggunakan metode Mean Absolute Percentage Error (MAPE) dan pengukuran nilai akurasinya. Dari penelitian yang sudah dilakukan, disimpulkan bahwa produk Serum Glowing yang harus diproduksi pada bulan November 2024 sebanyak 130pcs. Nilai MAPE yang dihasilkan adalah sebesar 0,5% dan akurasi yang didapatkan adalah sebesar 99,5%. Dengan kata lain, penelitian ini dinilai sangat baik karena akurasi yang tinggi, sehingga diharapkan dapat memberikan gambaran pada klinik kecantikan Nastyaderm Karawang agar pengambilan keputusan dalam kebutuhan stok barang dapat diantisipasi dengan lebih tepat.
ANALISIS KUALITAS WEBSITE PORTAL MEDIA ONLINE MILENIANEWS.COM MENGGUNAKAN STANDAR ISO 9126 Firdaus, Muhammad Rifqi; Alkhalifi, Yuris; Ismunandar, Dinar; Kurniawan, Oky
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.v19i2.6218

Abstract

Software quality can be assessed based on two main criteria, namely conformance to specifications and the ability to meet user needs. One of the international standards used to assess software quality is ISO 9126, which includes six main aspects: functionality, reliability, usability, efficiency, maintainability, and portability. In this journal, four aspects are taken to examine the quality of an online media portal website milenianews.com. The research methods include black-box testing for functionality, stress testing for reliability, Likert Scale-based questionnaire for usability, and GTMetrix for efficiency. The results showed that the functionality aspect scored 100%, indicating that all functions run according to specifications. The reliability aspect shows a 100% success rate on sessions, pages, and hits, indicating excellent performance under high usage conditions. Usability scored 79%, which falls into the good category, reflecting an interface that is easy to use and understand by users. The efficiency aspect obtained grade B with a performance score of 75% and structure 91%, indicating quite good performance, although there is room for improvement, especially in the load time of 2.5 seconds and total blocking time of 192 ms. Overall, the milenianews.com online media portal has met ISO 9126 quality standards and is declared suitable for use. These results show the importance of implementing international standards-based quality testing to ensure an optimal user experience.
PENERAPAN DECISION TREE DENGAN PENYEIMBANGAN DATA IMBALANCE MENGGUNAKAN UPSAMPLING DALAM PREDIKSI PENYAKIT LIVER Agung Fazriansyah; Yuris Alkhalifi; Ainun Zumarniansyah
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.v19i2.6369

Abstract

Acute liver disease has a significant impact on liver function and is often only detected at an advanced stage due to the lack of patient awareness for early examination.  One of the challenges in treating liver disease is the delay in diagnosis, where many patients do not notice the early symptoms until their condition has worsened.  Therefore, a predictive system is needed that can identify liver disease patients early on, allowing for regular check-ups and timely treatment.  In this study, a classification model was developed using a machine learning approach, specifically the Decision Tree algorithm, by balancing the data in the minority class through upsampling.  The research results show that this model is capable of predicting liver disease status with an accuracy rate of 89.22%, a recall of 88.45%, a precision of 83.21%, and an f1-score of 85.78%.  In addition, the ROC-AUC value of 0.89 is categorized as a good classification.  This model achieved a higher accuracy score than other studies with similar datasets.  This system is expected to help improve early detection and expedite the treatment of liver disease patients.
MENJADI KREATOR DIGITAL DENGAN AI: INOVASI PELATIHAN KONTEN STOCK BAGI KOMUNITAS KOPIA MAMPANG JAKARTA SELATAN Yuris Alkhalifi; Khairul Rizal; Amir; Nur Alam
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 3 No. 3 (2025): Juni
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v3i3.2320

Abstract

Perkembangan teknologi kecerdasan buatan (AI) menghadirkan peluang baru dalam dunia kewirausahaan digital (digitalpreneurship) yang terus berkembang pesat. Menjawab tantangan rendahnya literasi AI di kalangan generasi muda, khususnya di lingkungan KOPIA, disusun program pelatihan “Menjadi Kreator Digital dengan AI: Inovasi Pelatihan Konten Stock bagi Komunitas Pemuda KOPIA”. Kegiatan ini bertujuan meningkatkan keterampilan peserta dalam menggunakan AI untuk membuat konten digital kreatif, mengunggah karya di platform stock konten seperti Freepik dan Shutterstock, serta membangun portofolio digital yang berdaya saing. Materi pelatihan mencakup pengenalan digitalpreneurship berbasis AI, praktik penggunaan Canva AI, Midjourney, Leonardo AI, ChatGPT, dan tools editing lainnya, serta strategi monetisasi karya digital melalui berbagai platform daring. Metode pelaksanaan dilakukan secara terstruktur dalam tiga tahap, yaitu persiapan, pelaksanaan pelatihan, serta monitoring dan evaluasi berkelanjutan. Pelatihan berlangsung satu hari penuh dengan pendekatan praktik langsung yang intensif dan interaktif. Target capaian meliputi peningkatan kemampuan peserta dalam penggunaan tools AI, pembuatan akun kontributor, pengunggahan minimal satu karya, serta terbentuknya komunitas pendukung pasca pelatihan untuk berjejaring. Melalui program ini, diharapkan peserta memiliki keterampilan dasar di bidang konten digital berbasis AI, mampu membuka peluang ekonomi baru secara mandiri, serta berkontribusi aktif dalam pengembangan literasi teknologi di masyarakat luas. Kegiatan ini juga mendukung misi KOPIA dalam membentuk generasi muda yang mandiri, kompetitif, inovatif, kreatif, dan berjiwa sosial di era transformasi digital yang dinamis.
IMPLEMENTATION OF SUPPORT VECTOR MACHINE, PARTICLE SWARM OPTIMIZATION, AND NAÏVE BAYES ALGORITHMS IN SENTIMENT ANALYSIS OF PRODUCT REVIEWS: A CASE STUDY OF E-COMMERCE LAZADA Mery Oktaviyanti Puspitaningtyas; Kartika Puspita; Yuris Alkhalifi; Yulita Ayu Wardani
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.362

Abstract

Sentiment analysis is pivotal in deciphering customer opinions and attitudes towards products on e-commerce platforms such as Lazada. Machine learning algorithms like Support Vector Machine (SVM), SVM with Particle Swarm Optimization (PSO), and Naïve Bayes (NB) are leveraged to automate this process, aiding decision-making in business settings. This study specifically aims to assess the performance of SVM, SVM + PSO, and NB in analyzing sentiment from Lazada product reviews, focusing on key metrics like accuracy and Area Under the Curve (AUC). Using a dataset of Lazada reviews, each algorithm is rigorously trained and evaluated. SVM achieves 72.74% accuracy and an AUC of 0.893, while integrating PSO boosts accuracy significantly to 84.84% with an AUC of 0.898. In contrast, NB achieves 75.34% accuracy and an AUC of 0.663. These results highlight SVM + PSO's superior performance in sentiment classification compared to SVM and NB. The findings suggest that SVM + PSO presents a robust solution for sentiment analysis in e-commerce, surpassing traditional SVM and NB methods in accuracy and AUC metrics. This underscores the potential of optimization techniques like PSO to enhance machine learning algorithms for effective sentiment analysis in practical e-commerce applications.
Brain Tumor Classification based on Convolutional Neural Networks with an Ensemble Learning Approach through Soft Voting Puspita, Kartika; Ernawan, Ferda; Alkhalifi, Yuris; Kasim, Shahreen; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4609

Abstract

The brain is a vital organ that serves various purposes in the human body. Processing sensory data, generating muscle movements, and performing complex cognitive tasks have all historically relied heavily on the brain. One of the most common conditions affecting the brain is the growth of abnormal tissue in brain cells, leading to the development of brain tumors. The most common forms of brain tumors are pituitary, glioma, and meningioma, which are major global health issues. From these issues, there is a need for appropriate and prompt handling before the brain tumor disease becomes more severe. Quick handling is through an early disease detection approach, and computer vision is one of the trending early disease detection methods that can predict diseases using images. This research proposes a model in computer vision, namely the Convolutional Neural Network (CNN), with a soft voting ensemble learning strategy to classify brain tumors. The dataset consists of 7,023 images without tumors and MRI brain tumors such as glioma, meningioma, and pituitary with a resolution of 512x512 pixels. This experiment investigates classifier models such as VGG16, MobileNet, ResNet50, and DenseNet121, each of which has been optimized to maximize performance. The proposed soft voting ensemble strategy outperformed existing methods, with an accuracy of 97.67% and a Cohen's Kappa value of 0.9688. The proposed soft voting ensemble method approach has proven effective in improving the accuracy.
Analisis Perbandingan Metode SMART Dan MOORA Pada Pemilihan Karyawan Terbaik Klinik Kecantikan Yuris Alkhalifi; Muhammad Rifqi Firdaus; Dinar Ismunandar; Irwan Herliawan
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1620

Abstract

An employee is a person who works for a particular company or institution. One of the activities that a company or agency often does to motivate its employees to show their best performance is to select the best employees. Making the best employee decisions with simple calculations is often found problematic due to the plethora of considerations of several factors, one of which is the factor that describes how important the assessment weight of one criterion with the business weight of another. The problem was found at Karawang's Nastyaderm Beauty Clinic. Then the solution needed to solve the problem is to use decision-making technology such as the Decision Support System. (SPK). The aim of this research is to find the best employees by using a comparison of two SPK methods namely SMART and MOORA to find out which are the best method rated, easy to apply and relevant. The number of employees that will be evaluated is as many as 5 people. The evaluation was given by the owner of the Beauty Clinic Nastyaderm Karawang, the mother of Dr. Lina Wijaya. The results of research and testing of 5 employees obtained the highest score on the SMART method on behalf of Tiara Anggraeni with a score of 0.725 or 72.5%. By obtaining results from both methods, it provides an overview of the results of the two methods and can help provide alternative solutions to the owner of a beauty clinic to determine the best employee of his working environment
Mobile-based Application Development on Admission of New Students with Design Science Research Methodology Alkhalifi, Yuris; Ramadan, Rino; Atmaja, Rahdian Kusuma; Ispandi, Ispandi
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.4765

Abstract

Increased use of mobile devices in recent years has led to a change in human behavior as users. Mobile devices today are being used for a wide range of sectors ranging from entertainment, and business to education. In the field of education, it can be used to interact between teachers and students, and lecturers with students, and can also be done for registration of New Student Admission. The presence of PMB registration through mobile devices can help prospective students apply wherever they are without having to come directly to the campus. It's not implemented by the Indonesian Siber University. (Cyber University). The Cyber University campus is currently implementing New Student Admission registration directly through the campus, so this process is still likely to take a long time. To solve the problem, this study will solve the problem of new student enrolment that is still being done manually to be digitized by building mobile-based applications. The method to be used is the Design Science Research Methodology (DSRM) known as the fast method because it includes the Agile software development model. The programming language used is the Dart-based Flutter framework. As a result of the research carried out, the mobile-based PMB application on the Cyber University was successfully constructed and in line with expectations. Candidate students can download the app on the Google PlayStore with the keyword Cyber PMB
Grocery Shopping Sebagai Media Promosi Usaha Mikro Kecil dan Menengah pada Desa Suro Berbasis Website dengan Metode RAD Ispandi, ispandi; Rino Ramadan; Rahdian Kusuma Atmaja; Yuris Alkhalifi
SATIN - Sains dan Teknologi Informasi Vol 10 No 1 (2024): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/stn.v10i1.1115

Abstract

Kita sering mengalami masalah dalam mencari obat apotek Usaha Mikro Kecil dan Menengah (UMKM) ialah usaha perdagangan yang dikelola oleh perorangan yang merujuk pada usaha ekonomi produktif dengan kriteria yang sudah ditetapkan dalam Undang Undang. Terdapat banyak anggota UMKM yang sudah tergabung kedalam UMKM desa. Upaya desa meningkatkan penjualan masih kurang dirasakan manfaatnya dalam meningkatkan omset penjualan produk yang di tawarkan. Grocery Shopping adalah kegiatan berbelanja bahan makanan sehari-hari, toko pangan, dan toko bahan makanan. Kegiatan promosi yang dilakukan dalam kegiatan dimulai dari persiapan, observasi, foto produk, pembuatan website. Dengan pembuatan media promosi ini di harapkan mampu memberikan kebermanfaatan pada desa suro. Aplikasi yang di buat adalah media promosi produk yang di jual. diharapka aplikasi dapat berjalan dengan baik dan menghasilkan informasi yang dibutuhkan sebagai meida promosi. Berdasarkan nilai rata-rata SUS, dapat disimpulkan bahwa skor termasuk dalam kategori sangat baik.
Co-Authors Achmad Fachrurozi Achmad Rifai Agung Fazriansyah Agung Fazriansyah Ainun Zumarniansyah Aldo Erianda, Aldo Amir Amir Amir Amir Amir Anas Faisal Annisa Elfina Augustia Annisa Elfina Augustia Destia Sari Rahmadhani Fadillah Dinar Ismunandar Dinar Ismunandar Dipa Putra Pratama Ferda Ernawan Firdaus, Muhammad Rifqi Firstianty Wahyuhening Fibriany Fitra Septia Nugraha Gata, Windu Harsono, Muhammad Luthfiy Kurniawan Hasan Basri Imam Budiawan Irmawati Irmawati Irwan Herliawan Ispandi Ispandi, Ispandi Kartika Puspita Kartika Puspita khairul rizal Khairul Rizal Kudiantoro Widianto, Kudiantoro Mayangky, Nissa Almira Mery Oktaviyanti Puspitaningtyas Mohammad Syamsul Azis Muhammad Ilham Prasetya muhammad luthfiy kurniawan harsono Muhammad Luthfiy Kurniawan Harsono Muhammad Rifqi Firdaus Nila Hardi Nur Alam Nurajijah Nurajijah Oky Kurniawan Prasetya, Arfhan Puspita, Kartika Qudsiah Nur Azizah Rahdian Kusuma Atmaja Resi Taufan Rian Ardianto Rifki Sadikin Rino Ramadan Shahreen Kasim, Shahreen Tika Adilah M Tri Rivanie Windu Gata Windu Gata Yan Rianto Yulita Ayu Wardani Zahrah Rachmawaty