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All Journal Jurnal Masyarakat Informatika Techno.Com: Jurnal Teknologi Informasi Prosiding SNATIF JAIS (Journal of Applied Intelligent System) Journal of Information System Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat Journal of Soft Computing Exploration
Lalang Erawan
Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Penerapan Algoritma Apriori Untuk Membantu Calon Mahasiswa Dalam Memilih Program Studi Di Fakultas Ilmu Komputer Universitas Dian Nuswantoro Marshela Dinda Amalia; Lalang Erawan
JOINS (Journal of Information System) Vol 2, No 2 (2017)
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (332.288 KB) | DOI: 10.33633/joins.v2i2.1677

Abstract

Abstrak Menuut Educational Psychologist dari Integrity Development Flexibility (IDF) Irene Guntur, M.Psi., CGA, sebanyak 87%mahasiswa di Indonesia salahjurusan.Sering terjadi ketidakseimbangan antara jumlah mahasiswa yang diterima dengan jumlah mahasiswa yang lulus tepat waktu. Pada data Fakultas Ilmu Komputer Udinus angkatan 2008 – 2012 sebanyak 25% mahasiswa lulus tepat waktu sedangkan mahasiswa yang lulus terlambat sebanyak 75%. Data mining adalah proses penemuan pola yang terdapat dalam suatu data dengan jumlah yang besar. Dengan memanfaatkan data mahasiswa yang sudah lulus maka akan menghasilkan satu setitem acuan untuk membantu mahasiswa dalam memilih program studi dan pada penelitian ini calon mahahsiwa dapat mengetahui status kelulusannya kelak. Penelitian ini menggunakan minimal support 20%  dan minimal confidance 50%.Jika kombinasi itemset tidak memenuhi syarat minimal support dan minimal confidance maka itemset tersebut akan dieliminasi. Hasil yang di peroleh untuk program studi  Teknik Informatika satu itemset acuan, Sistem Informasi 2 itemset acuan, Desain Komunikasi Visual 2 itemset acuan, Teknik Informatika-D3 satu itemset acuan, dan broadcasting-D3 satu itemset acuan. Kata kunci :Algoritma Apriori, prediksi, Mahasiswa, Rekomendasi Abstract According Educational Psychologist from Integrity Development Flexibility (IDF) Irene Guntur, M.Psi., CGA, as many as 87% of students in Indonesia are wrong majors. The factor of the graduating student is one of the majors. There is often an imbalance between the number of students received and the number of students who graduate on time. In the data of the Faculty of Computer Science Udinus class of 2008 to 2012 as many as 25% of students graduated exactly while the passing of students is late 75%. Data mining is the process of finding patterns in a large number of data. By utilizing the data of students who have graduated it will produce a setitem reference to help mahahsiwa in choosing a course of study and in this study prospective mahahsiwa can know the status of his graduation later. This research has minimum support  20% and minimum confidance 50%. If the combination of itemset is not qualified it will be eliminated.The results obtained are Informatics Engineeringhas one reference itemset, Information System has 2 reference itemset, Visual Communication Design has 2 reference itemset, Informatics Engineering - D3 has one reference itemset and D3 broadcasting has one reference itemset.  Keywords :Apriori Algorithm, prediction, Student, Recommendation  
REKAYASA MODEL SISTEM INFORMASI WEB SERTIFIKASI KOMPETENSI DI LEMBAGA SERTIFIKASI PROFESI MENGGUNAKAN METODOLOGI MODELDRIVEN UWE (UML-BASED WEB ENGINEERING) Lalang Erawan; Ajib Susanto; Agus Winarno
Prosiding SNATIF 2015: Prosiding Seminar Nasional Teknologi dan Informatika
Publisher : Prosiding SNATIF

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

AbstrakTenaga kerja Indonesia yang kompeten semakin penting menjelang pelaksanaan Asean Economic Community (AEC) pada tahun 2015. Pemerintah memastikan kompetensi tenaga kerja melalui program sertifikasi kompetensi yang dilaksanakan oleh Lembaga Sertifikasi Profesi (LSP) yang ditunjuk oleh BNSP (Badan Nasional Sertifikasi Profesi). LSP bertanggung jawab terhadap pengembangan standar kompetensi, sertifikasi kompetensi, dan pelaksana akreditasi Tempat Uji Kompetensi (TUK). Sistem manajemen berteknologi informasi diperlukan untuk mendukung operasional LSP agar efisien, cepat, dan produktif. Sistem web telah menjadi salah satu platform yang paling sering digunakan sebagai basis suatu sistem. Pendekatan pengembangan model-driven diyakini paling tepat untuk rekayasa web. Metode pendekatan sistem yang digunakan yaitu UWE (UML-Based Web Engineering) karena kompatibilitasnya dengan alat UML yang sudah akrab di kalangan pengembang sistem dan mencakup seluruh siklus pengembangan. Penelitian ini menghasilkan suatu alternatif model sistem manajemen sertifikasi kompetensi dan lisensi LSP yang dengan pendekatan model-driven rancangan sistem bersifat flesibel sehingga relatif mudah penerapannya diberbagai LSP yang meskipun sebagian besar struktur dan prosedur sertifikasinya sama tetapi tetap ada keunikan di masing-masing LSP. Data penelitian diperoleh dari sejumlah LSP, asesor, asesi, dan TUK.Kata kunci: Model Sistem, LSP, sertifikasi kompetensi, UML-Bases Web Engneering
Sistem Informasi Fee Streamer Berbasis Web Pada Shark Agency Dengan Pendekatan Extreme Programming Erawan, Lalang; Afif, Rizal Naufal; Winarno, Agus; Suharnawi, Suharnawi
JOINS (Journal of Information System) Vol. 7 No. 2 (2022): Edisi November 2022
Publisher : Program Studi Sistem Informasi, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v7i2.6676

Abstract

Shark Agency merupakan organisasi yang bergerak di bidang outsourcing pada platform voice dan video streaming yang menjembatani lebih dari lima aplikasi streaming dengan para streamer. Pengelolaan data laporan dan pembayaran fee kepada streamer dilakukan dengan menggunakan berbagai aplikasi yang berbeda misalnya pengiriman data dari streamer dilakukan dengan aplikasi whatsapp. Akibatnya data laporan dari para streamer kadangkala hilang dan harus dikirim ulang. Layanan seperti ini mengurangi efisiensi dan kepuasan para streamer. Dengan mengimplementasikan sistem yang tepat permasalahan ini akan dapat diselesaikan.  Metode yang digunakan untuk mengembangkan sistem adalah extreme programming yang memungkinkan sistem dapat dikembangkan lebih cepat. Fitur sistem yang dikembangkan dibatasi pada pengelolaan data laporan dan pembayaran fee streamer. Pengembangan menghasilkan sistem informasi yang dapat menerima data laporan streamer kemudian diolah untuk menjadi dasar perhitungan pembayaran fee kepada streamer. Data dapat tersimpan dengan aman disatu tempat sehingga resiko kehilangan data laporan dan pembayaran fee dapat diminimalisasi.
Pengembangan Sistem Informasi Penjualan Berbasis Web Dengan Metode Test Driven Development Pada CV IN-BOX Jepara Nur Raditya Mahardika; Erawan, Lalang
JOINS (Journal of Information System) Vol. 9 No. 1 (2024): Edisi Mei 2024
Publisher : Program Studi Sistem Informasi, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v9i1.9168

Abstract

CV IN-BOX Jepara memiliki bisnis penjualan packing box dimana data-data bisnis diolah menggunakan perangkat lunak spreadsheet yang menimbulkan berbagai permasalahan pengelolaan informasi klasik seperti akurasi data rendah, ketidaktepatan waktu pelaporan dan sebagainya. Perusahaan kemudian berinisiatif menyelesaikan permasalahan ini menggunakan teknologi informasi dengan mengembangkan sebuah sistem informasi penjualan berbasis web. Metode pengembangan sistem yang dipilih adalah Test Driven Development yang terbukti efektif mengembangkan sistem secara cepat dengan hasil yang lebih teruji. Sistem dikembangkan dengan membuat berbagai unit tes untuk menghasilkan fungsional sistem yang diharapkan. Penelitian berhasil mengembangkan sistem informasi penjualan berbasis web ini tanpa kendala yang berarti. Sistem baru diharapkan dapat menyelesaikan permasalahan pengelolaan informasi yang sedang dihadapi perusahaan saat ini.
Prediction of Sleep Disorders Based on Occupation and Lifestyle: Performance Comparison of Decision Tree, Random Forest, and Naïve Bayes Classifier Lestiawan, Heru; Jatmoko, Cahaya; Agustina, Feri; Sinaga, Daurat; Erawan, Lalang
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.8987

Abstract

Health is a very important thing in life. Therefore, to maintain health, we need adequate rest. Without adequate rest, the body will not be healthy and fit. In this study, a person's sleep disorder prediction will be made based on their lifestyle and work. The predictions made will classify sleep disorders that are absent, sleep apnea and insomnia from certain lifestyles and work. The methods used to make predictions are decision tree classifier, random forest classifier and naïve Bayes classifier. The test was carried out using a total of 375 data which was broken down into 70% training data and 30% testing data. The results obtained after testing with test data are by using the decision tree classifier algorithm to get an accuracy of 89.431%, using the random forest classifier algorithm to get an accuracy of 90.244% and by using the naïve Bayes classifier algorithm to get an accuracy of 86.992%.
Comparative Study of Classification of Eye Disease Types Using DenseNet and EfficientNetB3 Jatmoko, Cahaya; Lestiawan, Heru; Agustina, Feri; Erawan, Lalang
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 3, August 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i3.1931

Abstract

The purpose of this research is to build a classification model that can perform the eye disease identification process so that the diagnosis of eye disease can be known and medical action can be taken as early as possible. This research used a dataset which has a total of 4217 eye image data and had 4 main classes namely cataract, diabetic retinopathy, glaucoma, and normal. With the data distribution of 1038 cataract images, 1098 diabetic retinopathy images, 1007 glaucoma images, and 1074 normal images, which of this data will be divided with a data percentage scheme of 50:10:40, 60:10:30, and 70:10:20, to see the results of which dataset division can produce optimal accuracy. In this study, the classification process will use 2 CNN transfer learning architectures, namely DenseNet, and efficientnetb3, which are both trained using the ImagiNet dataset. The results obtained after completing the testing process on the model built using the DenseNet architecture get optimal accuracy when using data division as much as 60:10:30, which is 78.59% while using the efficientnetb3 architecture optimal accuracy results when using the data division of 70:10:20, which is 95.66%. In research on the classification that had previously been done, it is very rare to find a classification process for eye disease types, therefore, in this study, the classification process will be carried out and provide an overview of the eye disease classification process with the CNN transfer learning method with more optimal accuracy results.
Eye disease classification using deep learning convolutional neural networks Rachmawanto, Eko Hari; Sari, Christy Atika; Krismawan, Andi Danang; Erawan, Lalang; Sari, Wellia Shinta; Laksana, Deddy Award Widya; Adi, Sumarni; Yaacob, Noorayisahbe Mohd
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.493

Abstract

This study begins with the analysis of the growing challenge of accurately diagnosing eye diseases, which can lead to severe visual impairment if not identified early. To address this issue, we propose a solution using Deep Learning Convolutional Neural Networks (CNNs) enhanced by transfer learning techniques. The dataset utilized in this study comprises 4,217 images of eye diseases, categorized into four classes: Normal (1,074 images), Glaucoma (1,007 images), Cataract (1,038 images), and Diabetic Retinopathy (1,098 images). We implemented a CNN model using TensorFlow to effectively learn and classify these diseases. The evaluation results demonstrate a high accuracy of 95%, with precision and recall rates significantly varying across classes, particularly achieving 100% for Diabetic Retinopathy. These findings highlight the potential of CNNs to improve diagnostic accuracy in ophthalmology, facilitating timely interventions and enhancing patient outcomes. For future research, expanding the dataset to include a wider variety of ocular diseases and employing more sophisticated deep learning techniques could further enhance the model's performance. Integrating this model into clinical practice could significantly aid ophthalmologists in the early detection and management of eye diseases, ultimately improving patient care and reducing the burden of ocular disorders.
Improving Cervical Cancer Classification Using ADASYN and Random Forest with GridSearchCV Optimization Saputra, Resha Mahardhika; Alzami, Farrikh; Pramudi, Yuventius Tyas Catur; Erawan, Lalang; Megantara, Rama Aria; Pramunendar, Ricardus Anggi; Yusuf, Moh.
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2552

Abstract

Cervical cancer is a leading cause of death among women, with over 300,000 deaths recorded in 2020. This study aims to improve the accuracy of cervical cancer diagnosis classification through a combination of Adaptive Synthetic Sampling (ADASYN) and Random Forest algorithm. The research data was obtained from the Cervical Cancer dataset in the UCI Machine Learning Repository with an imbalanced data distribution of 95% negative class and 5% positive class. ADASYN method was chosen for its ability to handle imbalanced data by focusing on minority data points that are difficult to classify. The Random Forest algorithm was optimized using GridSearchCV to achieve maximum performance. Results show that this combination improved accuracy from 96.5% to 96.8% and recall from 93.7% to 94.3%. Feature importance analysis identified key risk factors such as number of pregnancies, age at first sexual intercourse, and hormonal contraceptive use that significantly influence diagnosis. This research demonstrates the effectiveness of combining ADASYN and Random Forest in enhancing classification performance for early cervical cancer detection.
Performance Analysis Cryptography Using AES-128 and Key Encryption Based on MD5 Pratama, Reza Arista; Rachmawanto, Eko Hari; Irawan, Candra; Erawan, Lalang; Laksana, Deddy Award Widya; Ali, Rabei Raad
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): Issue in Progress
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.75091

Abstract

The rampant misuse of data theft has created data security techniques in cryptography. Cryptography has several algorithms that are very strong and difficult to crack, including the AES (Advanced Encryption Standard) algorithm consisting of 128 bits, 192 bits, and 256 bits which have been proven resistant to conventional linear analysis attacks and differential attacks, then there is the MD-5 algorithm (Message-Digest algorithm 5) which is a one-way hash function by changing data with a long size and inserting certain data in it to be recovered. If the two are combined, it becomes more difficult to crack; therefore, to determine its performance, this study conducted a combination experiment of AES-128 with a key encrypted by MD-5, including avalanche effect tests, encryption and decryption execution times, and entropy values of encryption. The types of documents for testing are files with the extensions .docx, .txt, .pptx, .pdf, and .xlsx. After conducting tests on document files obtained from the processing time test, it shows that .txt and .pptx documents dominate with a fast process, while the longest process is obtained by .xlsx and .docx documents for all test files, then the avalanche effect test with an average of 98% and the entropy test is classified as good between values 3 - 7 which are close to value 8. This proves that the combination of the AES-128 algorithm with the MD-5 key can be used as an alternative for securing documents with stronger security, while maintaining standard processing times
Pelatihan Penerapan Trigger dan Stored Procedure Database Pada Siswa Sekolah Menengah Kejuruan Negeri 2 Semarang Winarno, Agus; Erawan, Lalang; Irawan, Candra; Muslih, Muslih; Suharnawi, Suharnawi; Arifin, Zaenal; Fahmi, Amiq
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 3 (2025): Vol 8, No 3 (2025): SEPTEMBER 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i3.3021

Abstract

SMK Negeri 2 Semarang yang berdiri sejak tahun 1951 terus beradaptasi untuk memenuhi tuntutan dunia industri, salah satunya dengan meningkatkan kompetensi siswa melalui Uji Kompetensi Keahlian (UKK). Salah satu tantangan yang dihadapi adalah masih terbatasnya pemahaman guru dan siswa jurusan Rekayasa Perangkat Lunak (RPL) mengenai pemanfaatan trigger dan stored procedure dalam pengelolaan basis data untuk efisiensi, keamanan, dan otomasi sistem informasi. Metode pelaksanaan pelatihan terdiri dari tahap persiapan, pelaksanaan, evaluasi, dan pelaporan kegiatan. Hasil evaluasi menunjukkan adanya peningkatan pengetahuan dan pemahaman yang signifikan. Pemahaman awal terhadap materi pelatihan berkisar antara 12,5% sampai dengan 37,5%. Setelah mengikuti pelatihan, baik guru maupun siswa menunjukkan peningkatan pengetahuan dan pemahaman sebesar 100%. Hasil penilaian menunjukkan bahwa pelatihan ini efektif dalam meningkatkan pengetahuan, pemahaman, dan kemampuan siswa dalam pengelolaan basis data khususnya trigger dan stored procedure . Simpulan dari pelatihan ini adalah keberhasilannya dalam menjembatani kesenjangan pengetahuan dan mendukung kesiapan siswa dalam memasuki dunia kerja industri.
Co-Authors Afif, Rizal Naufal Agus Winarno Agus Winarno, Agus Agustina, Feri Ajib Susanto Ali, Rabei Raad Alzami, Farrikh Cahaya Jatmoko Candra Irawan Christy Atika Sari De Rosal Ignatius Moses Setiadi Dhia Adikusuma Adikusuma Eko Hari Rachmawanto Fahmi Amiq Heru Lestiawan Krismawan, Andi Danang Laksana, Deddy Award Widya Marshela Dinda Amalia Maulydea Ramayanti Megantara, Rama Aria Moh. Yusuf, Moh. Muslih Muslih Nur Raditya Mahardika Pratama, Reza Arista Priskila Penny Ardiana Ricardus Anggi Pramunendar Saputra, Resha Mahardhika Sinaga, Daurat Sintia Rahma Herdajanti Suharnawi Suharnawi Suharnawi Suharnawi Suharnawi Sumarni Adi, Sumarni Tan Samuel Permana Wellia Shinta Sari Yaacob, Noorayisahbe Mohd Yudistira Dian Hastiti Yuventius Tyas Catur Pramudi Zaenal Arifin