Articles
<![CDATA[ANALISIS PERBANDINGAN DECISION TREE C4.5 DAN KNN DALAM PERIZINAN BONGKAR MUATAN KAPAL ]]>
<![CDATA[Nazifah, Naurah]]>;
<![CDATA[Prianto, Cahyo]]>;
<![CDATA[Awangga, Rolly Maulana]]>
<![CDATA[ JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 7 No. 3 (2023): JATI Vol. 7 No. 3 ]]>
Publisher : <![CDATA[Institut Teknologi Nasional Malang ]]>
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DOI: 10.36040/jati.v7i3.6889
<![CDATA[Classification Decision Tree merupakan salah satu metode populer dalam analisis data dan pembelajaran mesin. Algoritma C4.5 adalah salah satu algoritma decision tree yang banyak digunakan karena kemampuannya dalam menghasilkan aturan keputusan yang dapat dipahami dengan mudah. Perizinan bongkar muatan kapal adalah proses krusial dalam operasi pelabuhan yang memastikan kapal dapat secara efisien dan aman melakukan bongkar muatan dalam upaya untuk meningkatkan efisiensi dan mengoptimalkan pengambilan keputusan perizinan. Penelitian ini bertujuan untuk melakukan analisis perbandingan metode machine leraning antara algoritma decision tree C4.5 dengan algoritma K-Nearest Neighbors (KNN). Penulis sudah membandingkan kinerja algoritma-algoritma ini berdasarkan kriteria yang termasuk akurasi prediksi, dengan Classification Decision Tree menghasilkan peramalan unggul sebesar 98,33% dan 97,60% untuk algoritma KNN dalam investigasi ini. Hasil analisis bahwa pemilihan algoritma decision tree harus didasarkan pada tujuan spesifik analisis dan karakteristik data yang digunakan. Jika interpretabilitas aturan keputusan menjadi faktor utama, algoritma C4.5 tetap menjadi pilihan yang baik. Namun, jika akurasi prediksi dan penanganan data yang tidak seimbang menjadi prioritas, algoritma KNN dapat menjadi pilihan yang lebih baik.]]>
<![CDATA[E-learning Academy Untuk Meningkatkan Kapasitas SDM Di Lingkungan Perusahaan Transportasi X ]]>
<![CDATA[Andarsyah, Roni]]>;
<![CDATA[Prianto, Cahyo]]>
<![CDATA[ Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024) ]]>
Publisher : <![CDATA[Politeknik Harapan Bersama ]]>
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DOI: 10.30591/jpit.v9i3.7835
<![CDATA[ Services in the field of land transportation services are still a sector needed by the community for mobility and economic growth. The main problem faced by Transportation Company X in developing human resources (HR) is the uneven skills and knowledge between generations in the organizational structure. Gen X dominates with 57.36%, Gen Y (38.34%) and Gen Z (4.29%). This has an impact on the ability to adapt to the demands of the modern transportation industry. This research aims to develop and implement an e-Learning Academy, to increase the capacity of X Transportation Company's human resources. This research methodology uses SCRUM framework in learning system development, with agile approach that allows adaptation to changes quickly and efficiently. E-Learning Academy features video-based learning and interactive elements that allow employees to learn independently, thus maximizing knowledge transfer and improving skills in various fields. Survey results after testing by users through user acceptance test activities show that on the Ease of Navigation aspect, 55% of respondents stated “strongly agree” the application is easy to use”. The aspect of Confidence in Application Capabilities, the results are 55% of respondents “strongly agree” this application believes it can improve HR skills and abilities. For the Quality of Main Features, 36% of respondents stated “strongly agree” the main features in this application are easy to use and the remaining 64% stated “agree”. On the aspect of Impact on HR Improvement, 46% of respondents “strongly agree” this application has a positive impact and the remaining 54% of respondents stated “agree”. Finally, on the aspect of Benefit for the Company, 36% of respondents “strongly agree” that this application is useful and the remaining 64% stated “agree”. This platform can be accessed across all business sectors so that it becomes a strategic tool that helps Transportation Company X achieve its goals and improve its public transportation services.]]>
<![CDATA[Implementasi Algoritma Gunning Fog Index Untuk Mengukur Tingkat Keterbacaan Tugas Akhir Mahasiswa Menggunakan Pemrograman Python ]]>
<![CDATA[Riza, Noviana]]>;
<![CDATA[Supriady, Supriady]]>;
<![CDATA[Setiadi, Hilman]]>;
<![CDATA[Prianto, Cahyo]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v6i1.6176
<![CDATA[This research is motivated by the importance of abstracts in a scientific work as a key element that provides an overview of the content of the research. Abstracts are a key element in scientific work, and their readability is important so that the research message can be well understood by readers. However, students' abilities in writing abstracts vary greatly. Some students still have difficulty in compiling abstracts that comply with the rules, which affects the readability and understanding of their research by readers. In addition, there are also students who are already proficient in making abstracts. Therefore, this study aims to measure the level of readability of students' final project abstracts and identify the factors that influence it using the Gunning Fog Index. This study involves the analysis of 100 abstracts from various departments at the University of Logistics and International Business. A web-based application will be built using Python. The model created will be implemented in the form of an application to make it easier for users to find out the level of readability. The implementation results show that the average Gunning Fog Index of the 100 abstracts analyzed was 9.2564, which means that the abstracts can generally be understood by readers with an education level equivalent to grade 9 of junior high school. The majority of abstracts (68%) were categorized as easy to read, while 9% were in the moderate category and 23% were difficult. The analysis also showed variations in readability levels between departments, with Study Program D having the highest average Gunning Fog Index and Study Program A having the lowest. Overall, this implementation successfully demonstrated the readability levels of students’ abstracts and provided insight into variations in writing quality between departments.]]>
<![CDATA[IMPLEMENTASI LINEAR PROGRAMMING PADA MODEL CVRPP UNTUK PENGELOLAAN OPERASIONAL LOGISTIK ]]>
<![CDATA[Adiningrum, Nur Tri Ramadhanti]]>;
<![CDATA[Prianto, Cahyo]]>;
<![CDATA[Setyawan, Muhammad Yusril Helmi]]>
<![CDATA[ Jurnal Informatika Vol 8, No 4 (2024): JIKA (Jurnal Informatika) ]]>
Publisher : <![CDATA[University of Muhammadiyah Tangerang ]]>
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DOI: 10.31000/jika.v8i4.12068
<![CDATA[Perusahaan logistik merupakan perusahaan yang memiliki kekhususan dalam penyediaan layanan logistik, yang membantu dalam mengelola fungsi rantai pasokan termasuk pergudangan, distribusi, dan transportasi. Salah satu perusahaan logistik di kota Bandung,yang bergerak di bidang layanan jasa logistik dan memiliki layanan pickup yang bertugas untuk memasarkan produk serta melakukan layanan penjemputan barang. Pada layanan ini, tahap perencanaan aktivitas seperti rute dan kapasitas kendaraan merupakan tahapan yang penting. Namun, pada penerapannya perusahaan ini belum menerapkan aktifitas perjalanan dengan rute terbaik atau hanya berdasar pengalaman driver, serta kurang memaksimalkan kapasitas angkut kendaraan. Capacitated Vehicle Routing Problem with Pickup (CVRPP) adalah metode yang digunakan dalam penanganan masalah ini. Penelitian ini bertujuan pada pembuatan model pencarian jarak dan pemaksimalan kapasitas kendaraan yang dapat meningkatkan efisiensi operasional dalam pengelolaan kapasitas dan rute logistik. Untuk mencapai tujuan penelitian, Linear Programming dengan bahasa pemrograman Python digunakan sebagai proses perhitungan yang digunakan dan menghasilkan solusi terbaik. Hasil penelitian menunjukkan bahwa rute yang terbentuk menggunakan Linear Programming menghasilkan jarak paling pendek diantara rute lainnya dengan penghematan jarak sebesar 19.99% pada analisis dan 31.92% pada aplikasi. Hal itu juga didukung dengan evaluasi dengan Optimality Gap yang bernilai 0% atau solusi yang ditemukan adalah optimal atau sangat baik.]]>
<![CDATA[Sentiment Analysis of Hate Speech Against Presidential Candidates of the Republic of Indonesia in the 2024 Election Using BERT ]]>
<![CDATA[Amalia, Fahriza Rizky]]>;
<![CDATA[Nisa Hanum Harani]]>;
<![CDATA[Cahyo Prianto]]>
<![CDATA[ Jurnal Sistem Cerdas Vol. 7 No. 3 (2024) ]]>
Publisher : <![CDATA[APIC ]]>
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DOI: 10.37396/jsc.v7i3.432
<![CDATA[The issue of hate speech on social media has become a matter of growing concern, particularly in the context of political discourse, as evidenced by the 2024 elections in Indonesia. Online platforms such as YouTube represent a primary medium for political discourse, frequently accompanied by negative or hateful commentary directed towards presidential candidates. The objective of this study is to analyze the sentiment of YouTube comments related to Indonesian presidential candidates in the 2024 General Election using the BERT algorithm. The data was obtained through scraping using the YouTube API and subsequently categorized into three distinct categories of hate speech: The categories of hate speech are as follows: OFP (offensive personal), OFG (offensive group), and OFO (offensive others). The CRISP-DM method was employed in this research, which included the following stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results demonstrate that the BERT algorithm is capable of classifying comments with a satisfactory level of accuracy. This algorithm can be utilized to develop predictive applications that assist in identifying and managing hate speech on social media.]]>
<![CDATA[Probability Prediction for Graduate Admission Using CNN-LSTM Hybrid Algorithm ]]>
<![CDATA[Zuhri, Burhanudin]]>;
<![CDATA[Harani, Nisa Hanum]]>;
<![CDATA[Prianto, Cahyo]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v12i3.3248
<![CDATA[Currently, the prediction of student admissions still uses conventional machine learning algorithms where there is no algorithm for optimization. This study aims to produce a model that can predict student acceptance of ownership more optimally by using an optimization hybrid learning algorithm, namely the Convolutional Neural Network Long Short Term Memory (CNN-LSTM). This study uses the Microsoft Team Data Science Process method which consists of business understanding, data acquisition & understanding, modeling, and implementation as well as using the acceptance dataset obtained from the kaggle.com website as much as 500 data. The results showed that the CNN-LSTM hybrid learning model could optimize the prediction of students' chances of success in exposure as evidenced by the evaluation results of RMSE of 6.31%, MAE of 4.4%, and R2 of 80.52%. This model is implemented in a website application using the Python language, the Django framework, and the MySQL database.]]>
<![CDATA[Performance Analysis and Development of QnA Chatbot Model Using LSTM in Answering Questions ]]>
<![CDATA[Ilyas Tri Khaqiqi, M]]>;
<![CDATA[Harani, Nisa Hanum]]>;
<![CDATA[Prianto, Cahyo]]>
<![CDATA[ The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science ]]>
Publisher : <![CDATA[AI Society & STMIK Indonesia ]]>
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DOI: 10.33022/ijcs.v12i3.3249
<![CDATA[This research aims to evaluate the performance of a Long Short-Term Memory (LSTM) based chatbot in answering questions (QnA). LSTM is a type of Recurrent Neural Network (RNN) architecture specifically designed to overcome vanishing gradient problems and can store long-term information. The method used is 5-fold cross-validation to train the chatbot model with 15 epochs at each fold using the dataset provided. The results showed variations in model performance at each fold. At the 5th fold, there was a decrease in performance with 84.63% accuracy, 96.36% precision, 64.9% recall, and 69.84% loss value. This finding shows that there is variability in the performance of the QnA chatbot model at each fold. In conclusion, the LSTM chatbot model can provide good answers with high accuracy and precision. Still, performance variations need to be considered in the use of this chatbot.]]>
<![CDATA[Penerapan Augmented Reality Sebagai Media Promosi Menggunakan Algoritma Regresi Logistik ]]>
<![CDATA[Prianto, Cahyo]]>;
<![CDATA[Harani, Nisa Hanum]]>;
<![CDATA[Andarsyah, Roni]]>
<![CDATA[ Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 2 (2023): Edisi Agustus ]]>
Publisher : <![CDATA[STIKOM Tunas Bangsa Pematangsiantar ]]>
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DOI: 10.30645/jurasik.v8i2.653
<![CDATA[Nowadays, some companies use social media to promote their product, with no exception PT. Pos Indonesia also promotes hiring an influencer to become a brand ambassador for introducing Pos and its product. In this era, digital marketing has an important influence on business. A unique way is needed to get attention and increase interaction between customers and posted content. For fulfilling that thing, a promotion app with Augmented Reality is designed. This technology combines the virtual and real world at the same time, in Indonesia itself promotion of AR is still seldom. By using AR, the PT Pos promotion package will be shown in the form of 3D objects when the Logo of PT. Pos is highlighted with Augmented Reality Camera. Then the promo could be shared using social media to emerge a bond with the user, so the user will get a poin that is managed by a logistic regression algorithm. Users will feel involved in promotion and also gain benefits in the form of poins so, indirectly there will be a lot of people who promote the product of PT. Pos voluntarily. Modeling using logistic regression is done with 1498 data, 75% of the data becomes the data train and 25% of the rest becomes the data test, the created model has an accuracy 61.07%.]]>
<![CDATA[Pengembangan Sistem Manajemen Transaksi dan Stok Barang dengan Pendekatan Agile ]]>
<![CDATA[Kamaluddin, Rendy]]>;
<![CDATA[Muhammad Yusuf, Hadi]]>;
<![CDATA[Prianto, Cahyo]]>
<![CDATA[ Competitive Vol. 18 No. 2 (2023): Jurnal Competitive ]]>
Publisher : <![CDATA[PPM Universitas Logistik dan Bisnis Internasional ]]>
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DOI: 10.36618/competitive.v18i2.4104
<![CDATA[Aplikasi Point of Sales (POS) berbasis web dikembangkan untuk meningkatkan efisiensi operasional dan pengelolaan transaksi pada toko baju. Sistem ini dibangun menggunakan Laravel dan MySQL serta mencakup manajemen pengguna, produk, transaksi, gudang, dan laporan. Aplikasi mendukung tiga peran utama: admin mengelola pengguna dan laporan, kasir memproses penjualan, dan staf gudang mengawasi pergerakan stok. Pengembangan menggunakan metodologi Agile untuk memastikan fleksibilitas melalui iterasi dan evaluasi berkelanjutan. Pengujian unit memastikan keandalan sistem dalam aspek fungsionalitas dan antarmuka pengguna. Fitur utama mencakup pelacakan stok real-time, pencatatan barang masuk dan keluar, serta notifikasi stok rendah untuk mencegah kekurangan inventaris. Aplikasi ini meningkatkan akurasi transaksi, efisiensi pengelolaan stok, dan mendukung pengambilan keputusan yang lebih cepat dan tepat]]>
<![CDATA[ANALISIS SENTIMEN KELUHAN PEGAWAI DENGAN MENGGUNAKAN MACHINE LEARNING ]]>
<![CDATA[Syahra, anita alfi]]>;
<![CDATA[Mohamad Nurkamal Fauzan]]>;
<![CDATA[Cahyo Prianto]]>
<![CDATA[ MULTINETICS Vol. 10 No. 2 (2024): MULTINETICS Nopember (2024) ]]>
Publisher : <![CDATA[POLITEKNIK NEGERI JAKARTA ]]>
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DOI: 10.32722/multinetics.v10i2.6894
<![CDATA[PT Dirgantara Indonesia (PTDI) faces challenges in managing employee complaints. This research aims to improve PTDI employee complaint management through sentiment analysis using the Naive Bayes algorithm with the CRISP-DM method. The stages applied include business understanding, data understanding, data preparation, modeling, evaluation and implementation. Employee complaint data is collected and processed using stop words removal and tokenization techniques. The Naive Bayes model is trained and evaluated using accuracy, precision, recall and F1-score metrics. The research results show that the Naive Bayes model is effective in grouping employee complaints into mild and severe categories. The model has an accuracy of 88.5%. The implementation of this sentiment analysis system is expected to help PTDI management handle employee complaints more quickly and precisely, increasing satisfaction and productivity. This research also contributes to the development of the science of sentiment analysis and machine learning, as well as its application in complaint management in companies. With this system, PTDI management can identify and prioritize complaints that require immediate handling, increasing operational efficiency and service quality to employees. This research provides practical solutions for PTDI and adds insight into the application of machine learning in managing employee complaints.]]>
