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All Journal Jusikom : Jurnal Sistem Komputer Musirawas JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Teknologi Informasi MURA J-SAKTI (Jurnal Sains Komputer dan Informatika) JUSIM (Jurnal Sistem Informasi Musirawas) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI J-SAKTI (Jurnal Sains Komputer dan Informatika) BEES: Bulletin of Electrical and Electronics Engineering Livestock and Animal Research Prosiding Seminar Riset Mahasiswa Jurnal Teknologi Informasi Mura Journal Global Technology Computer Jurnal Komputer dan Teknologi (JUKOMTEK)
Karman, Joni
Universitas Bina Insan, Bekasi
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PENERAPAN ALGORITMA NAÏVE BAYES CLASSIFIER PADA PREDIKSI STOK OBAT DI APOTEK K-24 KOTA LUBUKLINGGAU DENGAN PEMROGRAMAN PHYTON Egiyansya, Aag; Karman, Joni; Satrianansyah, Satrianansyah; Elmayati, Elmayati
Jusikom : Jurnal Sistem Komputer Musirawas Vol 9 No 1 (2024): Jusikom : Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v9i1.2268

Abstract

tidak terjadi kekurangan maupun kelebihan persediaan obat yang Ada beberapa Indikator prediksi adalah bulan penjualan, stok barang, permintaan barang dan status barang. Sistem prediksi yang dapat digunakan untuk memprediksi berapa stok obat yang harus disediakan dimasa yang akan datang dengan menggunakan metode klasifikasi naïve bayes. Naïve bayes clasiffier didasarkan pada teorema Bayes yang memiliki kemampuan klasifikasi serupa dengan decision tree dan neural network. Bayesian classification terbukti memiliki akurasi dan kecepatan yang tinggi saat diaplikasikan ke dalam database dengan data yang besar Dari pengujian model yang ada didapatkan nilai untuk akurasi yaitu sebesar 91 %, presisi sebesar 90 %, recall sebesar 89 % dan F1 score sebesar 90 %. Dari data tersebut maka model yang dihasilkan dapat digunakan untuk memprediksi stok obat dimasa yang akan datang.
ANALISIS TINGKAT PENERIMAAN PENGUNA APLIKASI LIVIN BY MANDIRI MENGGUNAKAN METODE TECHNOLOGY ACCEPTANCE MODEL (TAM) STUDI KASUS UNIVERSITAS BINA INSAN LUBUKLINGGAU Ndaru, Rere Ingga Dewa; Karman, Joni; Alamsyah, Muhammad Nur
Prosiding Seminar Riset Mahasiswa Vol 1, No 1: Maret 2023
Publisher : Universitas Islam Sultan Agung

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Abstract

Kemajuan teknologi informasi, khususnya internet, memberikan dampak positif dan kemudahan dalam berbagai aktivitas sehari-hari. Hal ini terlihat pada munculnya berbagai bisnis online yang mengandalkan kecanggihan teknologi internet. Bank Mandiri memanfaatkan teknologi internet dengan menyediakan aplikasi mobile banking resmi bernama Livin By Mandiri untuk mempermudah customer dalam melaksanakan transaksi perbankan secara online. Aplikasi Livin by Mandiri mengintegrasikan semua jenis rekening, termasuk tabungan, pinjaman personal, dan kartu kredit, serta terhubung dengan ekosistem digital lainnya seperti e-wallets. Metode Technology Acceptance Model (TAM) digunakan dalam analisis ini. Dengan Livin by Mandiri, nasabah dapat melakukan pembayaran dan transfer dengan satu aplikasi sajaKeyword: kemajuan teknologi; technology acceptance model (TAM).; APP livin by mandiri e-bangking; e-wallets; mobile banking.
ANALISIS SENTIMEN MASYARAKAT TERHADAP KENAIKAN HARGA BBM DI MEDIA SOSIAL TWITTER MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Fauzan, Muazim Rahman; Wijaya, Harma Oktafia Lingga; Satrianansyah, Satrianansyah; Karman, Joni
Prosiding Seminar Riset Mahasiswa Vol 1, No 1: Maret 2023
Publisher : Universitas Islam Sultan Agung

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Abstract

Melalui media sosial twitter, masyarakat bisa berbagi cuitan tentang kesehariannya, berbagi foto, berita terbaru, cuaca, politik, ataupun menyampaikan pendapat tentang suatu hal yang sedang trending topic. Salah satu isu yang sedang trending topic di twitter pada bulan september 2022 di indonesia adalah harga BBM naik. Indonesia secara resmi menaikan harga BBM dikarenakan meroketnya harga minyak dunia. Sehingga pemerintah tidak dapat lagi menjual harga BBM yang sama seperti sebelumnya kepada masyarakat, hal ini dilakukan pemerintah untuk mengurangai pengeluaran negara yang semakin membesar. Penelitian dilakukan untuk mengetahui sentimen masyarakat terhadap kenaikan harga BBM pada tweets pengguna media sosial twitter menggunakan metode SVM. Data yang di ambil adalah 5000 data tweets yang menggunakan kata kunci “harga bbm naik”. Pada tahap preprocessing dari 5000 data, tersisa menjadi 4209 data. Lebelling data dilakukan otomatis menggunakan library TextBlob. Hasil labelling data menggunakan library TextBlob dibagi menjadi 3 sentimen yaitu sentimen positif sebanyak 1275, sentimen negatif sebanyak 1287, dan sentimen netral sebanyak 1647. Klasifikasi dan evalusi dilakukan menggunakan metode SVM. Berdasarkan nilai perbandingan data training dan testing sebesar 9:1, 8:2, dan 7:3 diperoleh hasil yang paling tinggi menggunakan metode SVM adalah perbandingan data training dan testing 9:1 dengan tingkat akurasi sebesar 76%.Keyword: Analisis Sentimen, harga BBM, Support Vector Machine, Twitter.
ANALISIS KEPUASAN PENGGUNAAN PELAYANAN PERIZINAN ONLINE “SI CANTIK” PADA DINAS PENANAMAN MODAL DAN PELAYANAN TERPADU SATU PINTU KOTA LUBUKLINGGAU MENGGUNAKAN METODE IMPORTANCE PERFORMANCE ANALYSIST (IPA) Rahmawati, Diah Ayu; Karman, Joni; Sobri, Ahmad
Prosiding Seminar Riset Mahasiswa Vol 1, No 1: Maret 2023
Publisher : Universitas Islam Sultan Agung

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Abstract

E-Government memberikan pelayanan publik terhadap masyarakat. Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu Kota Lubuk Linggau, telah menggunakan teknologi pelayanan perizinan online yaitu SICANTIK. Kendala yang dirasakan oleh pegawai, seperti gangguan yang terjadi akibat maintenance system mempengaruhi aktivitas pelayanan. Dilakukan analisis terhadap aplikasi SiCantik cloud pada DPMPTSP Kota Lubuklinggau. Memperhatikan faktor yang mempengaruhi kepuasan pemakai digunakan sebagai saran serta masukan untuk memperbaiki atau meningkatkan kualitas dan kinerja dari aplikasi tersebut secara maksimal. Metode Importance Performance Analysist (IPA) sebagai alat ukur yang digunakan untuk mengetahui tingkat kepuasan penggunaan aplikasi. Aplikasi diukur dengan tingkat kepentingan dan tingkat kinerja menggunakan 3 variabel sebagai indikator yaitu Kualitas Informasi, Kualitas Interaksi, dan Kemudahan terdapat 100 responden. Hasil pemetaan nilai gap yaitu sebesar 0,04 dimana nilai variabel Kemudahan sebesar  -0 07, variabel Kualitas Informasi 0,01, variabel Kualitas Interaksi -0,07. Kemudian dilakukan pemetaan setiap item pada diagram kartesius dengan sumbu X (performance) sebesar 3,81 dengan sumbu Y (importance) sebesar 3,85. Kepuasan terhadap kualitas layanan aplikasi SiCantik DPM-PTSP Kota Lubuklinggau dilihat dari kemudahan, kualitas informasi, dan kualitas Interaksi memiliki kinerja yang baik serta dapat memenuhi harapan pengguna. Hasil pemetaan diagram diperoleh 2 indikator yang perlu di tingkatkan, 7 indikator yang perlu dipertahankan oleh DPM-PTSP Kota Lubuklinggau. Terdapat 11 indikator yang tidak dianggap penting pada kuadran C dan D.Keyword: Analisis, Importance Performance Analysist, Kepuasan Pengguna
KLASIFIKASI JENIS PENYAKIT BUAH MANGGA BERBASIS DEEP LEARNING MENGGUNAKAN ARSITEKTUR RESNET DAN MOBILENET Cornelis Rasyid, Nanda; Karman, Joni; Toyib Hidayat, Asep; Lingga Wijaya, Harma Oktavia
Jurnal Komputer dan Teknologi Vol 5 No 1 (2026): JUKOMTEK JANUARI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i1.570

Abstract

Mango plantations in Indonesia face significant challenges due to pests and diseases that reduce productivity and cause economic losses for farmers. Manual identification of these issues requires expert knowledge and is often time-consuming and inaccurate. This study aims to develop a classification system for detecting various mango leaf diseases using deep learning models, specifically ResNet and MobileNet architectures. Deep learning, particularly Convolutional Neural Networks (CNNs), enables automatic disease detection from plant images by learning patterns without explicit programming. The proposed system focuses on identifying common diseases such as leaf blight, whiteflies, and leaf caterpillars efficiently and accurately. By leveraging image-based recognition, the system allows for early diagnosis and timely intervention. The results of this research are expected to provide a technological solution that supports modern agriculture and empowers farmers with better disease management tools.
Model Hybrid dalam Penentuan Stok Barang Bangunan Melalui Pendekatan Machine Learning Adinda, Intan Bintang; Irawan, Davit; Karman, Joni; Sobri, Ahmad
Journal of Computer System and Informatics (JoSYC) Vol 7 No 1 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i1.8065

Abstract

This study aims to develop a machine learning-based construction material stock prediction model using a hybrid approach that combines K-Means Clustering as a sales pattern grouping method and Support Vector Machine (SVM) as a classification method to predict material sales levels. This research was motivated by the problem of stock management at Toko Usaha Jaya in Lubuklinggau City, which is still done manually, thus potentially causing excess stock that increases storage costs and stock shortages that can lead to lost sales opportunities and decreased customer satisfaction. The data used includes material names, initial stock quantities, quantities sold, remaining stock, and selling prices collected during the period from January to December 2023. The results show that the hybrid model is capable of grouping materials into three categories, namely very popular, fairly popular, and less popular, with a Silhouette Score of 0.42, indicating fairly good clustering quality. Furthermore, the SVM model produced a classification accuracy rate of 99%, reflecting an increase in stock prediction accuracy compared to manual management methods. These findings indicate that the application of the K-Means and SVM hybrid model can improve inventory management efficiency and support more accurate and effective data-driven decision making.
Deteksi Berita Hoax Berbahasa Indonesia Menggunakan Multilingual-BERT pada Media Berita Online Tarissa, Syalma; Akbar, Muhamad; Martadinata, A. Taqwa; Karman, Joni
TIN: Terapan Informatika Nusantara Vol 6 No 11 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i11.9351

Abstract

The development of infoormation technology is increasing, which can trigger the spread of hoaxes in online newss media, resulting in misinformation for the public. Manual detection of hoaxes is difficult due to the large volume of online news and variety of language styles and mixed language usage. Advances in artificial intelligence technology, particularly Natural Language Processing (NLP), open up significant opportunities for automatically detecting hoax news. Transformer-based NLP models, such as Multilingual-BERT (mBERT), are emerging. Transformer-based models such as Multilngual-BERT (mBERT) are capable of understanding text context in both directions and support various languages, including Indonesian. Therefore, this study aims to apply and test the effectiveness of mBERT in detecting hoax news in online news media. The dataset used comes from TurnbackHoax on the Kaggle website for hoax news, Tempo and CNN Indonesia for non-hoax news datasets, and additional data from the Kaggle website on news coverage in 2025. The data was processed using the built-in mBERT Tokenizer with data undersampling techniques, resulting in a model with an accuracy of 0.97, a precision value of 0.96, a recall of 0.97, and F1-score of 0.97,which shows that the mBERT approach is able to provide high classification performance on the Indonesian language hoax news dataset.
Farmers’ behavior and the potential results of cattle-oil palm integration in South Sumatra's oil palm replanting area Herwenita, Herwenita; Karman, Joni; Hanapi, Sidiq; Irsan, Fuadi; Eliza Maryana, Yeni; Suprihatin, Agus; Hutapea, Yanter; Suparwoto, Suparwoto
Livestock and Animal Research Vol 22, No 1 (2024): Livestock and Animal Research
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/lar.v22i1.70731

Abstract

Objective: Replanting of oil palm plantations needs to be done to ensure long-term supply. However, problems arise due to the reduced income of farmers during the non-productive period of oil palm plantations, so there are various farmers’ behaviors in responding to this situation. One effort to increase farmers' income is through cattle-oil palm integration. This paper aims to analyze farmers’ behavior, the factors that influence it, and the potential yield obtained from the integration of cattle into oil palm in replanting fields.Methods: The research was conducted using case studies in Cinta Damai Village, Sungai Lilin District, Musi Banyuasin Regency, South Sumatra, from November 2019 to November 2020. The survey was conducted on 35 farmers to assess behavior and factors that could influence it. Meanwhile, the potential yield of cattle-oil palm integration was measured through a demonstration plot of 1 ha of odot grass planting and processing of cow manure waste. Behavioral data were analyzed using path analysis, while the potential yield was analyzed descriptively.Results: The results showed that farmers have behaviors that support the cattle-oil palm integration business. Factors that directly influence this behavior are land area and socio-economic motivation. Meanwhile, socio-economic motivation through success belief has a significant effect on farmers’ behavior.Conclusions: The farmers' behaviour regarding the implementation of cattle-oil palm integration in oil palm replanting areas had criteria of poor, medium and good, with percentages of 5.71%, 25.71% and 68.51% respectively. The factors that directly influence this behaviour are the area of oil palm replanting area and the socio-economic motivation of the farmers. The potential results obtained from implementing cattle-oil palm integration are 1) forage for livestock (FFL) from odot grass, and 2) manure and liquid organic fertilizer (LOF) from cow dunk.
Co-Authors Adinda, Intan Bintang Agus Suprihatin Alhadi, Muhammad Reyhan Anggardha, Alvin Anggoro, Dimas Bayu Armanto, Armanto Astri, Jemi Cahyadi, Setiaji Cornelis Rasyid, Nanda Darmawan, Muhammad Albani Daulay, Nelly Khairani Egiyansya, Aag Eliza Maryana, Yeni Elmayati, Elmayati Fauzan, Muazim Rahman Hanapi, Sidiq Herwenita, Herwenita Hidayat, Asep Toyib Intan, Bunga Irawan, Davit Irsan, Fuadi Lingga Wijaya, Harma Oktafia Lingga Wijaya, Harma Oktavia Martadinata, A. Taqwa Muhamad Akbar Muhammad Nur Alamsyah Mulyono, Hardi Mulyono, Harfi Ndaru, Rere Ingga Dewa Prigana, Akbar Rachman Juliandi, Dicky Rahmawati, Diah Ayu Rizki, Fido Rusdiyanto Saputra, Julius Satrianansyah, Satrianansyah sobri, ahmad Suparwoto Suparwoto Tarissa, Syalma Toyib Hidayat, Asep Ulawiya, Reza Wulandari, Cindi wulandari, Hardianti Yanter Hutapea