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All Journal TEKNIK INFORMATIKA JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA Infotech Journal JUPITER (Jurnal Pendidikan Teknik Elektro) CESS (Journal of Computer Engineering, System and Science) SMATIKA Jurnal Informatika Upgris Fountain of Informatics Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of Technology And Business Khadimul Ummah Journal of Social Dedication Jurnal Inotera JSiI (Jurnal Sistem Informasi) JURNAL MANAJEMEN (EDISI ELEKTRONIK) IJISTECH (International Journal Of Information System & Technology) Simtek : Jurnal Sistem Informasi dan Teknik Komputer Indonesian Journal of Community Services Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) INFORMASI (Jurnal Informatika dan Sistem Informasi) Jurnal Sistem Komputer & Kecerdasan Buatan G-Tech : Jurnal Teknologi Terapan TIN: TERAPAN INFORMATIKA NUSANTARA Infotech: Journal of Technology Information Journal of Computer Networks, Architecture and High Performance Computing Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Infotech: Jurnal Informatika & Teknologi International Journal of Community Service International Conference on Industrial Revolution for Polytechnic Education Buletin Sistem Informasi dan Teknologi Islam Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Teknik Silitek Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Management and Digital Business Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Duta Abdimas: Jurnal Pengabdian Masyarakat Proceeding of International Conference Health, Science And Technology (ICOHETECH) LOFIAN: Jurnal Teknologi Informasi dan Komunikasi Innovative: Journal Of Social Science Research Prosiding Seminar Informasi Kesehatan Nasional Journal Of Artificial Intelligence And Software Engineering Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi SmartComp
Nurchim Nurchim
Universitas Duta Bangsa Surakarta, Indonesia
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Penerapan Internet of Things Untuk Pemantauan dan Otomatisasi Pemberian Nutrisi pada Sistem Hidroponik Menggunakan Metode Fuzzy Tsukamoto Novianto, Novianto; Nurchim, Nurchim; Pamekas, Bondan Wahyu
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 2 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i2.7625

Abstract

Pada pertanian menggunakan teknik hidroponik, proses pemantauan dan penyesuaian nutrisi merupakan salah satu hal yang menjadi kunci keberhasilan pertanian. Kesalahan ketika proses pemantauan dan penyesuaian nutrisi dapat mengakibatkan hasil panen tidak optimal, bahkan mengalami kegagalan. Untuk mempermudah proses pemantauan dan penyesuaian nutrisi dapat dilakukan dengan memanfaatkan teknologi Internet of Things (IoT). Dengan menggunakan ESP32 yang dihubungkan dengan sensor TDS, sensor suhu, sensor pH dan sensor ultrasonic, data kondisi lingkungan berupa data kadar nutrisi air, suhu air, ph air dan volume air dapat diambil kemudian dikirimkan ke server. Sehingga nantinya proses pemantauan dapat dilakukan dengan mudah melalui aplikasi web. Untuk proses pemberian nutrisi, ESP32 yang dihubungkan dengan pompa nutrisi dapat digunakan untuk mengatur pemberian nutrisi. Proses pemberian nutrisi selanjutnya dapat diotomatisasi dengan menggunakan metode Fuzzy Tsukamoto. Hasil penelitian yang dilakukan, menunjukan bahwa penerapan metode Fuzzy Tsukamoto untuk otomatisasi pemberian nutrisi dapat bekerja dengan cukup baik. Dimana 9 dari 10 percobaan yang dilakukan mendapatkan hasil yang sesuai. Kemudian untuk proses otomatisasi menunjukan akurasi yang cukup tinggi, dimana semua percobaan mendapatkan hasil diatas 89%.
Pemodelan Prediksi Pergerakan Harga Saham United Tractors Menggunakan Pendekatan ARIMA Rahadian, Dwiki Rasya; Nurchim, Nurchim; Permatasari, Hanifah
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 2 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i2.7422

Abstract

Pasar investasi dan ekuitas sangat memengaruhi ekonomi kontemporer. Investasi saham memiliki risiko yang tinggi, tetapi mereka juga menawarkan hasil yang menjanjikan. Investor dapat cepat mengalami kerugian atau keuntungan besar. Penurunan atau kenaikan harga saham sangat memengaruhi keuntungan investor. Akibatnya, baik investor maupun pelaku bisnis membutuhkan perkiraan harga saham untuk membuat keputusan. Dalam artikel ini disajikan hasil menggunakan metode ARIMA karena cocok dengan tipe data time series dalam peramalan harga saham PT. United Tractors . Hasil yang didapat membuktikan bahwa model ARIMA memiliki tingkat keakuratan yang cukup baik dalam peramalan harga saham. Dari prediksi harga saham dihasilkan RMSE(Root Mean Square Error) 139.597, Dimana berfungsi untuk mengetahui nilai kesalahan dari hasil prediksi.
TRANSFORMASI MONITORING ABSENSI SISWA SEKOLAH DENGAN INTERNET OF THINGS Andrean, Fauzi; Nurchim, Nurchim; Susanto, Rudi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.14448

Abstract

Sistem absensi manual yang dilakukan guru dan hasil rekap absensi manual memiliki kekurangan diantaranya hilang atau rusaknya data absensi, kesalahan data, waktu absensi yang sedikit lama dan informasi absensi hanya dapat diketahui oleh orang tua saat pembagian rapor atau wali kelas melakukan home visit. Penelitian ini bertujuan untuk mentransformasi monitoring absensi siswa dengan IoT. Metode pengembangan yang digunakan adalah prototype dengan tahapan yaitu pengumpulan kebutuhan, membangun prototype, evaluasi prototype, mengkodekan sistem, uji sistem, evaluasi sistem, dan penggunaan sistem. Teknik pengumpulan data dengan wawancara, observasi dan studi literatur. Pengujian NodeMCU ESP32 menggunakan python. Hasil penelitian berupa alat absensi, web yang menampilkan rekap data absensi siswa mengikuti proses belajar dan notifikasi whatsapp yang masuk ke handphone orang tua siswa. Hasil pengujian menunjukkan proses absensi menjadi lebih cepat, siswa hanya perlu melakukan tab kartu dan absensi sudah tercatat lebih cepat 5,2 menit dari absensi manual, administrasi lebih terstruktur dengan adanya data pencatatan absensi nama siswa, mata pelajaran, hari tanggal dan jam absensi serta keterangan siswa terlambat absensi menandakan siswa terlambat mengikuti pembelajaran, kemudahan memperoleh hasil absensi yang akurat dengan ditampilkannya data absensi real time pada website yang cepat diakses, dan notifikasi whatsapp yang diterima orang tua siswa memberikan kemudahan memantau siswa mengikuti pembelajaran.
Penerapan Metode Regresi dalam Memprediksi Laba Bersih Penjualan Sari, Nur Avia Adenta; Nurmalitasari, Nurmalitasari; Nurchim, Nurchim
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.7986

Abstract

Unexpected changes in net profit can make it difficult for XYZ to control costs and make strategic decisions. This study proposes the use of linear regression as a forecasting method for net sales profit as a way to reduce uncertainty in financial planning and assist in business decision-making. This approach was chosen because of its ability to measure and explain the correlation between dependent factors, such as net profit, and independent variables, such as sales volume and operating costs. The analysis procedure includes data exploration, regression modeling, model performance evaluation, and visualization of prediction results, which can be conducted systematically. Considering the findings of this study's evaluation, the model demonstrated excellent performance in predicting net sales profit, with an evaluation result of Root Mean Squared Error (RMSE) of 27778.50, Mean Absolute Error (MAE) of 20084.71, Mean Absolute Percentage Error (MAPE) of 9.88 %. The main advantage of this forecasting is its ability to help businesses improve the accuracy of financial planning, manage operational costs, and develop focused business plans. Additionally, management can avoid overstocking or understocking, set reasonable sales targets, and adjust production levels to market demand through the use of accurate forecasts.
Comparative Analysis of Yolov11 and Mask R-CNN Models for River Water Level Detection Setrayana, Abiyyu; Nurchim; Eko Purwanto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6622

Abstract

Flooding is one of the most frequent natural disasters in Indonesia, particularly in densely populated areas such as urban regions. The main cause is the delayed response in anticipating rising river water levels. One contributing factor is the continued use of manual river water monitoring systems. However, these systems often face challenges under various lighting and weather conditions. This study presents a comparison of two segmentation models, YOLOv11 and Mask R-CNN, for river water level detection. These models are evaluated for their application in real-time water level monitoring systems for dams and rivers under diverse lighting conditions. Data was gathered from publicly available sources, including river monitoring CCTV footage and social media content related to river activities, followed by annotation for model training. The YOLOv11 model, implemented using the Ultralytics framework and PyTorch library, achieved a mean Average Precision (mAP) at IoU (Intersection over Union) 50-95 of 99.657% and recall of 99.930%, demonstrating exceptional detection accuracy. The Mask R-CNN model, developed with Detectron2, attained an Average Precision (AP) at IoU 50-95 of 98.620% and a recall of 99.200%, also exhibiting high accuracy. Both models were tested in real-time scenarios, where they accurately detected water-level objects, although challenges arose under complex environmental conditions such as low light or water turbidity. To further enhance model performance, future work will focus on incorporating diverse environmental data and optimizing model parameters. In conclusion, YOLOv11 model offers higher accuracy and better resource efficiency, making it more suitable for real-time water level monitoring applications.
Implementasi Algoritma K-Nearest Neighbor untuk Optimasi Pemberian Reward Siswa SMA Riyanto, Agus; Nurchim; Oktaviani, Intan
Jurnal Sistem Informasi Vol. 12 No. 2 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i2.11017

Abstract

Giving rewards to school students is one strategy to increase learning motivation and participation in school. However, the system used to give rewards is usually still conventional, so it often faces challenges in terms of objectivity and comprehensiveness of assessment criteria. This study aims to apply the K-Nearest Neighbor (KNN) algorithm as an optimization tool in determining student reward recipients in High Schools. The data used include the average report card value, moral values, parents' income, number of siblings and scores in non-academic activities. The KNN method was chosen because of its ability to classify based on the similarity of neighbor data. The research process begins with collecting historical student data, data normalization, determining the KNN model, and evaluating the model. The results of the study show that the KNN model is able to classify students with a certain level of accuracy in recommending the right reward category. The conclusion of this study is that the application of the KNN algorithm can provide a more structured and objective approach to the reward giving process, so that it can help schools make transparent decisions and in accordance with the principles of justice. This system is expected to increase the effectiveness of the reward program and encourage development for students. Keyword : K-Nearest Neighbor, Reward, Classification, Objectivity, Optimization
EVALUASI SISTEM INFORMASI BAGIAN FILING MENGGUNAKAN METODE PIECES DI RUMAH SAKIT PANTI WALUYO YAKKUM SURAKARTA Arif, Yunita Wisda Tumarta; Nurchim, Nurchim; Aulia, Sherina Revita; Nurhayati, Nurhayati
Prosiding Seminar Informasi Kesehatan Nasional 2023 : SIKesNas 2023
Publisher : Fakultas Ilmu Kesehatan Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/sikenas.vi.2963

Abstract

Rumah Sakit Panti Waluyo YAKKUM Surakarta memiliki sistem informasi bagian filing yang terintegrasi menjadi salah satu bagian dengan Sistem Informasi Manajamen Rumah Sakit (SIMRS) yang bernama e-filing. Sistem informasi ini diimplementasikan dari bulan April tahun 2021, berdasarkan survei pendahuluan, pengoperasian SIMRS bagian filing masih terjadi kendala dibutuhkannya waktu proses loading untuk menampilkan informasi pada modul menu sehingga menyulitkan petugas menggunakan sistem. Metode yang digunakan untuk mengevaluasi sistem informasi bagian filing adalah metode PIECES. Tujuan dilakukan evaluasi adalah untuk menilai kinerja sistem yang berjalan dengan metode PIECES berdasarkan aspek performance, information, economic, control, dan service. Hasil penelitian ini adalah berdasarkan penilaian responden adalah sistem informasi bagian filing di Rumah Sakit Panti Waluyo YAKKUM Surakarta secara keseluruhan dikategorikan sangat baik dengan rerata presentase 77,40%. Evaluasi sistem informasi bagian filing dari Aspek performance sangat baik presentase 79,13%, sistem mampu menghasilkan troughput, audibilitas, kelaziman komunikasi dan kelengkapan yang sangat baik. Aspek information sangat baik presentase 81,62%, dimana sistem mampu menyajikan informasi, akurasi informasi, aksesbilitas dan relevansi informasi yang sangat baik. Aspek economic Sudah baik presentase 67,74%, dimana reusabilitas sistem dapat dikembangkan sesuai dengan perkembangan perangkat lunak. Aspek control sangat baik presentase 79,4%, dimana keamanan mekanisme dalam mengontrol dan melindungi sistem sangat baik namun tingkat integritas akses data perlu untuk ditingkatkan. Aspek efficiency sangat baik presentase 76,62%, dimana usabilitas usaha untuk mempelajari sistem sangat baik. Aspek service sangat baik presentase 79,94%, dimana sistem memiliki akurasi serta reliabilitas yang sangat baik.
Design And Development of An Automated Watering System For Curly Red Chili Plants Based on Internet Of Things Saputra, Muchammad Yoga; Nurchim, Nurchim; Maulindar, Joni
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7263

Abstract

An ideal soil moisture level between 60% and 80% is crucial for the optimal growth of curly chili plants. However, manual watering methods by farmers are often inefficient and fail to maintain consistent soil moisture. This issue forms the basis of the current research, which aims to develop an automated Internet of Things (IoT)-based watering system for curly chili plants, integrated with a monitoring website. The research method involves using an ESP32 microcontroller connected to a soil moisture sensor and a DHT22 temperature sensor. The data collected by the sensors is sent in real-time to the Firebase Realtime Database as a cloud platform and then visually displayed on a monitoring website. System testing confirmed that the setup successfully monitored soil conditions and air temperature while also controlling a DC mini water pump via a relay for automated watering based on the received data. In conclusion, the implementation of this IoT technology is expected to assist farmers in Kadokan Village in conserving water usage, improving time efficiency, and enhancing the quality and quantity of curly chili production.
Sentiment Analysis of Fans Toward Brand Merchandise Releases Using Support Vector Machine (SVM) Munaiseche, Christian Imanuel; Nurchim, Nurchim; Cipto Utomo, Bangun Prajadi
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7264

Abstract

The release of merchandise by idol groups often sparks various emotional reactions among fans, particularly on social media platforms. This study investigates fan sentiment regarding the birthday merchandise release by JKT48 members on the X (Twitter) platform using the Support Vector Machine (SVM) algorithm. A total of 1,062 comments were collected using the Tweet Harvest tool and manually categorized into three sentiment classes: positive, neutral, and negative. The collected data underwent several pre-processing stages, including case folding, data cleansing, tokenization, and stopword removal. The text data were then transformed into numerical features using the Term Frequency-Inverse Document Frequency (TF-IDF) method. To address the class imbalance issue, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Experimental results show that the SVM model without SMOTE achieved an accuracy of 84.62% and an F1-score of 76.79%. After applying SMOTE, model performance improved significantly, with accuracy reaching 90.09% and F1-score increasing to 90.15%. Furthermore, the results of 5-fold cross-validation confirmed the positive impact of SMOTE in enhancing the model's ability to classify sentiment, particularly for underrepresented classes.
Pengembangan Model Prediksi Penjualan Ice Cream UMKM Jogja Menggunakan Metode Autoregressive Muhammad Rais Ramadhani; Nurmalitasari; Nurchim
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 11 No. 2 (2024): Jurnal Derivat (Agustus 2024)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jderivat.v10i2.6289

Abstract

There are Micro Small Medium Enterprises (MSMEs) Jogja is included in the new category because of its launch in early 2023, but it already has a total turnover of hundreds of millions. This MSME focuses on enlisted Ice Cream sales with dozens of Ice Cream menu variants. The unstable level of sales originating from trends or seasons becomes a separate enemy for business actors. This study aims to carry out the permanent sales of MSME Jogja using the autoregressive method. The steps taken are (1) data collection, (2) Calculation of ACF and PACF, (3) data processing, and (4) calculation of error values. The data used in the current study is the 8 -8-month sales transaction data. The results showed that the autoregressive method can predict the sale of MSME Jogja with a low error value of MAE = 0.18 and RMSE = 0.14. With this, Ice Cream sales predictions using the Autoregressive Method can be accepted, which results in sales predictions in the following month, May 2024, as many as 1,118 products were sold. Suggestions for further researchers to research each existing menu variant. Keywords: Prediction, Sales, Ice Cream, Autoregressive
Co-Authors Abdullah Abdullah Syaifudin Afu Ichsan Pradana Agus Riyanto Agustina Srirahayu Ahmad Qashid Husaini Ahmad Setiawan Al Mustofa, Muhammad Hafizh Andrean, Fauzi Andy Ariyanto Ardi Lestari, Sofiana Ardianto Pambudi Arif Wicahyanto Assidiq, Abdul Hafid Atina, Vihi Atmojo, Fattah Satrio Atmojo, Fernando Winantya Aulia, Sherina Revita Awang Long, Zalizah Bagus Muhammad Latif Bondan Wahyu Pamekas Bondan Wahyu Pamekas Carolina Wibowo, Anita Cipto Utomo, Bangun Prajadi Dwi Hartanti Dwi Hartanti Dwi Kurniawan Saputro Edy Kurniawan Eko Purwanto Eko Purwanto Faiq Muhammad, Nibras Feri Setiyono Gabriel Ardana Hasanah, Herliyani Herliyani Hasanah Ibnu Bagus Setiawan Ichsan Pradana, Afu immaculata yolia dewi Widayanti Indah Nofikasari Indriyas Kukuh Wijayanti Irawan, Etwin Hendri Joni Maulindar Krisna Joko Purjianto Kurniawan, Daniel Ade Mahendra Abdul Rahman, Rizqy Maskhul Ryan Ibrahim Maulindar, Joni Muhammad Nibras Faiq Muhammad Rais Ramadhani Mumu, Raul Galvin Rudolf Munaiseche, Christian Imanuel Muttaqi, Bagas Ningsih, Pipin Widya Novianto, Novianto Nugroho, Mohammad Yusuf Nurhayati Nurhayati Nurlita, Catarina Ivanda Nurmalitasari Nurmalitasari Nurmalitasari Nurmalitasari Nurmalitasari Oktaviani, Intan Pamekas, Bondan Wahyu Permatasari, Hanifah Pipit Vidianti Pradana, Afu Ichsan Pramono Pramono Pramono Prasetya, Ian Putra Prastyo, Okik Dwi Pratama, Wahyu Adi Purwanto, Eko Putra Prasetya, Ian Putra Prasetya Putra Pratama, Dita Putra, Wihan Perkasa Nugraha Ragil Saputro, Abdullah Rahadian, Dwiki Rasya Rudi Susanto Rukmini, Siti Santoso, Tri Djoko Saputra, Muchammad Yoga Sari, Nur Avia Adenta Setrayana, Abiyyu Sholeh, Ilham Singgih Purnomo Sopingi Sulistyo Wahyu S Sumarlinda, Sri Suryadi, Agung Suryani, Fajar Suryani, Fajar Suryani Taufik Hidayat Tejo Arum, Dinenda Tri Djoko Santosa Untoro, Fendi Uvi Firgianingsih Uvi Firgianingsih Widayanti, immaculata yolia dewi Wijayanti, Indriyas Kukuh Wijiyanto Wijiyanto Wijiyanto Wijiyanto, Wijiyanto Yommy Adhiwira Yudha Yunita Wisda Tumarta Arif Zalizah Awang Long Zalizah Awang Long