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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

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 â€“ 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%Â 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 Â 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 supportÂ 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. Â Keywords :Apriori Algorithm, prediction, Student, RecommendationÂ Â

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

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

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

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

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

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

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.

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

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.

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

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.

Pelatihan Pemanfaatan Google Sites Untuk Pembuatan Media Pembelajaran Berbasis Website Untuk Guru Dan Dosen Pada Perkumpulanprofesi Multimedia Dan Teknologi Informasi (PPMULTINDO) Jatmoko, Cahaya; Rakasiwi, Sindhu; Widya Laksana, Deddi Award; Erawan, Lalang; Rizqa, Ifan; Astuti, Erna Zuni
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 2 (2024): Juli : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/community.v4i2.535

Conveying appropriate information to be understood quickly and accurately is very important in various areas of life, both academic and non-academic. A teacher or lecturer is a teacher whose job is to educate and provide instruction to students or students. Data visualization is one way that can be used to present data. The advantage of this method is the availability of statistical graphics which can enrich the display of information so that the results are more interactive for the audience. Google Sites is a service owned by the Google company that can be used for e-learning. That way, the information becomes more appropriate to understand quickly and accurately.

Pemanfaatan Google Drive Untuk Backup dan Akses File Bersama Pada CV Berkah Cimandiri Makmur erawan, lalang; Agus Winarno; Candra Irawan
Jurnal Abdi Negeri Vol 1 No 2 (2023): September 2023
Publisher : Informa Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63350/jan.v1i2.11

CV. Berkah Cimandiri Makmur merupakan perusahaan pengiriman barang sembako dan kebutuhan pokok masyarakat Kalimantan yang melalui jalur laut dengan armada Dump truk melalui kapal laut dan sebaliknya dari kalimantan membawa barang barang hasil hutan seperti rotan, kayu serta yang lain untuk dikirim ke Semarang dengan memanfatkan Ekspedisi melalui kapal laut. Dalam pelaksanaan operasional administrasinya dibutuhkan penggunaan file yang terintegrasi yang tersimpan di perangkat apapun pada satu tempat yang aman serta sinkronisasi dan bagikan file yang tak terbatas dan fleksibel. Dengan memanfaatkan Google Drive yang memiliki fungsi antara lain dapat berbagi file, menyimpan link, membuat catatan dari Google keep, backup file, backup Chart WhatsApp, dan mengedit file merupakan solusi yang paling murah dan mudah dipahami. Tujuan Program Kemitraan Masyarakat ini untuk memberikan peningkatan pengetahuan, keterampilan para karyawan, sehingga akan meningkatkan kinerja dan pelayanan operasional perusahaan. Luaran Program Kemitraan Masyarakat ini antara lain peningkatan pengetahuan dalam bidang Teknologi Informasi, Jurnal Nasional serta Dokumentasi Video dan menggunakan 3 tahap pelaksanaan yaitu tahap pretest, pengkayaan dan pendalaman materi, dan post test.

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