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All Journal PROSIDING SEMINAR NASIONAL Jupiter JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal DISPROTEK Bulletin of Computer Science Research Jurnal Teknologi Informasi Cyberku
Akhmad Khanif Zyen
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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PENGENALAN CARA MERANCANG VOICE IP (VOIP) PADA SISWA SISWI SMK WALI SONGO PECANGAAN Adi Sucipto; R. Hadapiningradja Kusumodestoni; Akhmad Khanif Zyen
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2015: Prosiding Bidang Teknik dan Rekayasa The 2nd University Research Colloquium
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Voice over Internet Protocol, or better known as VoIP is a technology that uses the Internet Protocol to provide electronic voice communication and real-time, this technology makes the Internet media in order to make long-distance voice communications directly. Analogue voice signals, as you hear when communicating on the phone in the form of data analaog then converted into digital data and transmitted over the network in the form of data packets in real time.The use of voice over internet Procotol in a company or institution is very useful because in addition to much cheaper, using existing data networks, cabling is much more simple, more flexible and future development can be combined with the existing telephone network. Seeing the importance of the benefits of science and technology of voice over internet protocol for society in general and vocational students majoring in Computer Engineering and Networks (TKJ) in particular, one vocational school that has the Network Computer Engineering Department located in the district of Jepara regency Pecangaan is SMK Walisongo, the education and training on designing free calls using VOIP is very important to be disseminated and implemented.Keywords: VoIP, Internet Phone, Computer Networking
APLIKASI MULTIMEDIA PEMBELAJARAN TENTANG ALAT PERAGA EDUKATIF MENGGUNAKAN ADOBE FLASH R. Hadapiningradja Kusumodestoni; Akhmad Khanif Zyen; Zainul Arifin MA
Jurnal DISPROTEK Vol 6, No 2 (2015)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v6i2.268

Abstract

Plaything is a kind of instrument playing functionalized and used by children to form the object of intelligence brain by playing, so it can provide fun, add information, and can form the object of all aspects of development. Educational Viewer Tool is a kind of plaything that is deliberately designed and created specifically to improve the knowledge and education interests. Educational Viewer tool for this is still traditional and formless learning media. With the multimedia-based learning media is expected to help children learn early childhood or kindergarten in the form of the introduction of objects or letters quickly through an attractive visual appearance. This research was conducted by identifying problems and observations. Applications were compiled by procedures in which consists problem identification, feasibility studies, system requirements analysis, concept design, system implementation and system testing. The results are it produced a multimedia application as a brilliant learning medium with educational material props as media recognition of object and letters to students of PAUD and kindergarten of banyu putih Kalinyamatan region. Based on the experiment, it can be concluded that the application of this study can help the learning process to the students so that not only facilitating the learning process but also enhancing students' motivation to learn. Keywords: Educational Viewer tool, APE, Learning, Multimedia. Alat permainan adalah jenis alat bermain yang difungsikan dan digunakan oleh anak-anak untuk membentuk objek kecerdasan otaknya dengan cara bermain, sehingga dapat memberikan kesenangan, menambah informasi, serta dapat membentuk objek seluruh aspek pengembangannya. Alat Peraga Edukatif adalah suatu jenis alat permainan yang sengaja dirancang dan dibuat secara khusus untuk meningkatkan pengetahuan dan kepentingan pendidikan. Alat Peraga Edukatif selama ini masih bersifat tradisional dan belum berbentuk media pembelajaran. Dengan adanya media pembelajaran berbasis multimedia ini diharapkan dapat membantu anak PAUD atau TK dalam belajar pengenalan bentuk objek atau huruf dengan cepat melalui tampilan visual yang menarik. Penelitian ini dilakukan dengan cara mengidentifikasi permasalahan dan observasi. Aplikasi disusun dengan prosedur yang mencangkup identifikasi masalah, studi kelayakan, analisis kebutuhan sistem, perancangan konsep, implementasi system, dan pengujian sistem. Berdasarkan penelitian yang telah dilakukan, maka dihasilkan sebuah aplikasi multimedia sebagai media pembelajaran yang menarik dengan materi alat peraga edukatif sebagai media pengenalan bentuk objek dan huruf pada siswa siswi PAUD dan TK desa banyu putih kecamatan kalinyamatan. Berdasarkan hasil uji coba dapat disimpulkan bahwa aplikasi pembelajaran ini dapat membantu proses pembelajaran pada siswa-siswi sehingga tidak hanya memudahkan proses pembelajaran tetapi juga meningkatkan motivasi siswa dalam belajar. Kata Kunci : Alat Peraga Edukatif, APE, Pembelajaran, Multimedia.
PREDIKSI KECEPATAN ANGIN MENGGUNAKAN MODEL ARTIFICIAL NEURAL NETWORK BERBASIS ADABOOST Abdul Syukur; Catur Supriyanto; Akhmad Khanif Zyen
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 12 No 1 (2016): Jurnal Teknologi Informasi CyberKU Vol. 12, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Prediction is an attempt to predict the future by examining the past. This prediction consists of the bias estimation of the magnitude of future several variables, such as sales, on the basis of knowledge of the past, present, and experience. Adaboost is one of the optimization algorithm which can improve the accuracy of a predictive value. Previous research examines the exchange rate prediction of wind speed using back propagation Artificial Neural Network algorithm. The purpose of this study is intended to improve the accuracy of prediction of wind speed previously predicted using Artificial Neural Network Backpropagation algorithm then improved the prediction accuracy using adaboost algorithm during the process of training and added back propagation Artificial Neural Network algorithm in the learning process.The results showed that the prediction accuracy of the wind speed values previously predicted using Artificial Neural Network back propagation algorithm with an accuracy of prediction error at sample time per 10 minute predictions of 0.31576596 managed to reduce the value of the accuracy of the prediction error using adaboost algorithm during training and coupled Artificial Neural Network algorithm Backpropagation learning process with an accuracy of prediction error amounting to 0.15945762.
PREDIKSI PENENTUAN PEMOHON KREDIT SEPEDA MOTOR MENGGUNAKAN ALGORITMA NAIVE BAYES Nur Aeni Widiastuti; Akhmad Khanif Zyen; Nor Safik
Jurnal Disprotek Vol 10, No 2 (2019)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v10i2.4689

Abstract

Penentuan kelayakan pengajuan kredit motor pada sebuah perusahaan Dealer adalah hal yang sangat penting, mengingat jika terjadi kesalahan pengambilan keputusan maka akan berdampak pada kerugian perusahaan yang ada pada tempat penelitian ini yaitu Dealer Muncul jaya. Perlu adanya Algoritma Klasifikasi untuk memecahkan permasalahan ini, ada salah satu Algoritma Klasifikasi yang sudah terbukti akurasi dan kecepatan yang tinggi untuk  memecahkan permasalahan tersebut yaitu Algoritma Naive Bayes. Oleh karena itu penulis menganalisis Penentuan Kelayakan Pegajuan Kredit dengan metode Naïve Bayes untuk menentukan layak tidaknya sebuah pengajuan kredit motor. Penulis membuat perhitungan metode Naïve Bayes secara manual menggunakan Excel dan menggunakan aplikasi pendukung Rapidminer 5.3 untuk pengujian akurasi terhadap sistem yang buat. Pengujian dilakukan dengan menyiapkan data training sebanyak 180 data dan data testing sebanyak 33 data yang dipilih secara random. Didapat Hasil pengujian akurasi dengan Metode Naïve Bayes cukup tinggi yaitu sebesar 93,94% dengan persentase eror 6,06%. Jadi, dapat disimpulkan bahwa aplikasi yang dibuat dapat mendukung pengambilan keputusan penentuan kelayakan kredit motor.
Pengaruh Hyperparameter Tuning Gradient Boosting Terhadap Prediksi Pemilihan Program Studi Mahasiswa Baru Harminto Mulyo; Akhmad Khanif Zyen
Bulletin of Computer Science Research Vol. 5 No. 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i2.454

Abstract

This study aims to improve the accuracy of predicting new student major selection using the Gradient Boosting algorithm optimized through hyperparameter tuning. Gradient Boosting was chosen for its ability to handle complex and diverse data, which is crucial in the context of major prediction. The data used was sourced from the new student admissions database of Universitas Islam Nahdlatul Ulama Jepara for the 2013–2023 period, with preprocessing including data cleaning, imputation of missing values, and transformation of categorical features. The initial accuracy of the Gradient Boosting model with default configuration reached 99.01%, indicating that the dataset had relatively clear and structured patterns, enabling the baseline model to perform highly. However, to ensure generalization and avoid the risk of overfitting, hyperparameter tuning was performed using Randomized Search CV. The tuning results showed an increase in accuracy to 99.84% with optimal configurations including a learning rate of 0.1, 300 estimators, and a maximum tree depth of 4. Feature analysis also revealed that attributes such as "school_type," "school_origin," and "gender" significantly influenced the prediction outcomes. This study demonstrates that hyperparameter tuning can significantly enhance model performance, providing a more accurate and relevant predictive solution for the major selection process. Nevertheless, the study's limitation lies in the scope of the dataset, which originated from a single institution, suggesting the need for further exploration with more diverse data and advanced tuning methods like Bayesian Optimization. These findings provide valuable contributions to educational institutions in developing data-driven systems to support strategic decision-making.
Multiclass Sentiment Analysis of Electric Vehicle Incentive Policies Using IndoBERT and DeBERTa Algorithms Muhammad Bayu Nugroho; Akhmad Khanif Zyen; Nur Aeni Widiastuti
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9511

Abstract

The electric vehicle (EV) incentive policy in Indonesia has generated various public reactions, particularly on social media platforms. This study aims to classify public sentiment using the IndoBERT and DeBERTa transformer models. A total of 6,758 comments were collected from YouTube, filtered, preprocessed, and labeled into three sentiment categories: positive, negative, and neutral. From this, 1,711 clean data points were used and analyzed in two phases: before and after applying the Random Oversampling technique to address class imbalance. Model performance was evaluated using accuracy, precision, recall, F1-score, and training time. In the initial phase, IndoBERT achieved 96% accuracy with 603.71 seconds of training time, while DeBERTa reached 93% in 439.19 seconds. After balancing and applying 5-Fold Cross Validation, IndoBERT maintained 96% accuracy with balanced metric distribution, while DeBERTa recorded 93% accuracy. IndoBERT performed better in recognizing neutral sentiment, whereas DeBERTa was more time-efficient. These results highlight the effectiveness of local transformer models and data balancing techniques in improving sentiment classification performance on imbalanced datasets.
Optimasi Model Klasifikasi Diabetes dengan Stacking pada Algoritma XGBoost dan LightGBM Lukman Hakim; Akhmad Khanif Zyen; Sarwido
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 2 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract

 Diabetes Mellitus is a chronic metabolic disease that has a significant impact on public health due to the risk of serious complications, such as heart disease and kidney failure. Early detection is crucial to prevent these complications. The application of machine learning has proven effective in improving the accuracy of diabetes classification. This study aims to evaluate the effectiveness of the Stacking Ensemble technique compared to individual models, XGBoost and LightGBM, in classifying diabetes. The dataset used is the Diabetes Health Indicators from the CDC, consisting of 253,680 samples and 21 features. The preprocessing stages include normalization, class balancing using SMOTE, and an 80:20 train-test data split. The models were evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results show that the Stacking Ensemble achieved the highest accuracy (91.79%), followed by LightGBM (91.29%) and XGBoost (90.78%). The highest precision was achieved by the Stacking Ensemble (96.97%), while the highest recall was obtained by LightGBM (87.04%). These findings indicate that the ensemble learning method can enhance the accuracy of diabetes prediction, thereby supporting more accurate medical decision-making.  
Co-Authors A.A. Ketut Agung Cahyawan W Abdul Syukur Catur Supriyanto Lukman Hakim Muhammad Bayu Nugroho Mulyo, Harminto Nor Safik Nur Aeni Widiastuti Nur Aeni Widiastuti R Hadapiningradja Kusumodestoni Sarwido Zainul Arifin MA