Articles
<![CDATA[Evaluating Naïve Bayes Automated Classification for GBAORD ]]>
<![CDATA[Hani Febri Mustika]]>;
<![CDATA[Arida Ferti Syafiandini]]>;
<![CDATA[Lindung Parningotan Manik]]>;
<![CDATA[Yan Rianto]]>
<![CDATA[ Computer Engineering and Applications Journal Vol 9 No 1 (2020) ]]>
Publisher : <![CDATA[Universitas Sriwijaya ]]>
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DOI: 10.18495/comengapp.v9i1.320
<![CDATA[The Indonesian Government Budget Appropriations or Outlays for Research and Government (GBAORD) has been analyzed manually every year to measure the government expenditures in research and development. The analysis process involved several experts in making the budget classification. This method, commonly known as manual classification, has its downsides, which are time consumption and inconsistent result. Therefore, a study about implementing the machine learning method in GBAORD budget classification to avoid inconsistency is proposed in the previous research. For further analysis, this paper evaluates the performance of the Naïve Bayes algorithm for the GBAORD budget classification. This paper aims to measure the robustness of the Naïve Bayes to classify GBAORD data taken from 2017 until 2019. This paper uses three models of Naive Bayes with different preprocessing methods and features. This paper concludes that using the Naïve Bayes algorithm in Indonesian GBAORD budget classification is suitable since the robustness of the algorithm is proved to be high with 96.788+-0.185% average accuracy.]]>
<![CDATA[The Effect of DevOps Implementation on Teamwork Quality in Software Development ]]>
<![CDATA[Ady Hermawan]]>;
<![CDATA[Lindung Parningotan Manik]]>
<![CDATA[ Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April ]]>
Publisher : <![CDATA[Universitas Airlangga ]]>
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DOI: 10.20473/jisebi.7.1.84-90
<![CDATA[Background: The Agile method, which is claimed to reduce time needed for software development cycle has been widely used. It addresses communication gaps between customers and developers. Today, the DevOps has been extended as part of the Agile process to address communication gaps between developer’s team members. Despite the rising popularity, the effect of DevOps implementation on the teamwork quality in software development is still unknown.Objective: The objective of this research is to conduct a study on the impact of DevOps on teamwork quality. Two software houses, PT X and PT Y, are chosen as the case studies.Methods: This research uses quantitative methods to analyse research data using simple linear regression. The questionnaire technique is used to retrieve respondent data using 62 questions, consisting of 20 DevOps questions from 4 indicators and 42 teamwork quality questions from 6 indicators.Results: The results from various quality tests indicate that all instruments are valid and reliable while hypothesis tests showed that the DevOps implementation variable has an influence on the teamwork quality variable by 75.6%.Conclusion: It can be concluded that the implementation of the DevOps in software development has a positive correlation with the teamwork quality.]]>
<![CDATA[Non-Gaussian Analysis of Herbarium Specimen Damage to Optimize Specimen Collection Management ]]>
<![CDATA[Aris Yaman]]>;
<![CDATA[Yulia Aris Kartika]]>;
<![CDATA[Ariani Indrawati]]>;
<![CDATA[Zaenal Akbar]]>;
<![CDATA[Lindung Parningotan Malik]]>;
<![CDATA[Wita Wardani]]>;
<![CDATA[Tutie Djarwaningsih]]>;
<![CDATA[Taufik Mahendra]]>;
<![CDATA[Dadan Ridwan Saleh]]>
<![CDATA[ Knowledge Engineering and Data Science Vol 5, No 1 (2022) ]]>
Publisher : <![CDATA[Universitas Negeri Malang ]]>
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DOI: 10.17977/um018v5i12022p1-16
<![CDATA[Damage to specimen collections occurs in practically every herbarium across the world. Hence, some precautions must be taken, such as investigating the factors that cause specimen damage in their collections and evaluating their herbarium collection handling and usage policy. However, manual investigation of the causes of herbarium collection damage requires a lot of effort and time. Only a few studies have attempted to investigate the causes of herbarium collection damage. So far, the non-gaussian approach to detecting the causes of damage to herbarium specimens has not been studied before. This study attempted to explore the effect of species type, time, location, storage, and remounting status on the level of damage to herbarium specimens, especially those in the genus Excoecaria. Gaussian modeling is not good enough to model the counted data phenomenon (the amount of damage to herbarium specimens). Negative binomial regression (NBR) provides a better model when compared to generalized Poisson regression and ordinary Gaussian regression approaches. NBR detects non-uniformity in the storage process, causing damage to herbarium specimens. Natural damage to herbarium specimens is caused by differences in species and the origin of specimens.]]>
<![CDATA[Usability Aplikasi Kebencanaan di Indonesia Dengan Usability Testing dan Sistem Usability Scale ]]>
<![CDATA[Mochammad Zakiyamani]]>;
<![CDATA[Lindung Parlingotan Manik]]>
<![CDATA[ INTECOMS: Journal of Information Technology and Computer Science Vol 5 No 2 (2022): INTECOMS: Journal of Information Technology and Computer Science ]]>
Publisher : <![CDATA[Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE) ]]>
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DOI: 10.31539/intecoms.v5i2.5335
<![CDATA[Indonesia sebagai negara kepulauan berada pada posisi geografis, geologis, hidrologis, dan demografi yang rawan bencana. Secara geografis, 83% wilayah Indonesia (383 kab/kota) berpotensi risiko bencana tinggi. Apalagi hingga saat ini belum ada peneliti, sistem maupun alat yang mampu memprediksi kapan gempa akan terjadi. Walaupun demikian ada beberapa aplikasi kebencanaan yang ada di Indonesia yang dapat membantu memberikan informasi bencana. Namun aplikasi yang ada banyak terdapat keluhan pengguna khususnya masalah usability. Penelitian ini dilakukan untuk menganalisis keluhan pengguna dengan melakukan usability testing untuk mengetahui kelemahan serta menguji tingkat usability pada aplikasi kebencanaan di dalam mengukur variabel efektifitas, efisiensi, dan kepuasan dalam menggunakan aplikasi kebencanaan di Indonesia. Metode yang digunakan dalam penelitian ini adalah usability testing dengan teknik performance measurement dan restrsopective think aloud. Sedangkan responden yang digunakan dalam penelitian ini sebanyak 30 orang sebagai peserta uji dan 400 responden melakukan pengisian kuesioner. Hasil dari pengujian usability menunjukan bahwa nilai komponen efektivitas sebesar 89%, nilai komponen efisiensi sebesar 0,13 goals/second dan nilai kepuasan sebesar 63,22%.]]>
<![CDATA[Pemanfaatan Literasi Digital Alternatif Dalam Mencegah Hoax dan Penyebarannya Bagi Pelajar Yayasan Rukun Isteri Sejahtera ]]>
<![CDATA[Eni Heni Hermaliani]]>;
<![CDATA[Lindung Parningotan Manik]]>;
<![CDATA[Yan Rianto]]>;
<![CDATA[Ferda Ernawan]]>;
<![CDATA[, Alda Zevana Putri Widodo]]>;
<![CDATA[, Nurul Jannah Salsabila]]>
<![CDATA[ Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Vol 2 No 2 (2022) ]]>
Publisher : <![CDATA[Pengelola Jurnal Politeknik Negeri Ketapang ]]>
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DOI: 10.58466/literasi.v2i2.551
<![CDATA[Rukun Istri Sejahtera Foundation is a foundation engaged in social, educational, religious and humanitarian fields. The easy access to the internet and social media by many people, especially among students, has a bad influence as a negative effect of utilizing these innovations. More and more fake news that is often referred to as hoaxes can be accepted by the public, especially in the use of social media. In accessing digital media, you may encounter inappropriate things such as news and hoax information. Thus, students are very interested in acting out digital literacy in preventing hoaxes, including parents, teachers and all policymakers The problem that has been faced by partners is the difficulty in providing direction to students fostered by partners regarding the use of digital literacy as an alternative in preventing hoaxes and their spread. The solution for partners in overcoming these problems is to provide counseling on the use of digital literacy to prevent hoaxes and their spread to partner-assisted students. Based on these problems, this community service activity aims to develop knowledge and insights by providing counseling on the use of alternative digital literacy in preventing hoaxes and their spread to students of the Rukun Istri Sejahtera Foundation.]]>
<![CDATA[Aspect-Based Sentiment Analysis on Indonesian Presidential Election Using Deep Learning ]]>
<![CDATA[Fadillah Said]]>;
<![CDATA[Lindung Parningotan Manik]]>
<![CDATA[ Paradigma Vol. 24 No. 2 (2022): September 2022 Period ]]>
Publisher : <![CDATA[LPPM Universitas Bina Sarana Informatika ]]>
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DOI: 10.31294/paradigma.v24i2.1415
<![CDATA[The 2019 presidential election is a presidential election that has been a hot topic of discussion for some time, and people have even talked about this topic since 2018 on the internet. In predicting the winner of the presidential election, previous research has conducted research on the aspect-based sentiment analysis (ABSA) dataset of the 2019 presidential election using machine learning algorithms such as the Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN) and produces good accuracy. This study proposes a deep learning method using the BERT (Bidirectional Encoder Representation Form Transformers) and RoBERTa (A Robustly Optimized BERT Pretraining Approach) models. The results of this study indicate that the indobenchmark BERT and RoBERTa base-Indonesian single label classification models on target features with preprocessing produce the best accuracy of 98.02%. The indolem BERT model and the indobenchmark single label classification on the target feature without preprocessing produce the best accuracy of 98.02%. The BERT indobenchmark single label classification model on aspect features with preprocessing produces the best accuracy of 74.26%. The BERT indolem single label classification model on aspect features without preprocessing produces the best accuracy of 74.26%. The BERT indolem single label classification model on the sentiment feature with preprocessing produces the best accuracy of 93.07%. The BERT indolem single label classification model on the sentiment feature without preprocessing produces the best accuracy of 94.06%. The BERT indobenchmark multi label classification model with preprocessing produces the best accuracy of 98.66%. The BERT indobenchmark multi label classification model without preprocessing produces the best accuracy of 98.66%.]]>
<![CDATA[Software Effort Estimation Using Logarithmic Fuzzy Preference Programming and Least Squares Support Vector Machines ]]>
<![CDATA[Adnan Purwanto]]>;
<![CDATA[Lindung Parningotan Manik]]>
<![CDATA[ Scientific Journal of Informatics Vol 10, No 1 (2023): February 2023 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v10i1.39865
<![CDATA[Purpose: Effort Estimation is a process by which one can predict the development time and cost to develop a software process or product. Many approaches have been tried to predict this probabilistic process accurately, but no single technique has been consistently successful. There have been many studies on software effort estimation using Fuzzy or Machine Learning. For this reason, this study aims to combine Fuzzy and Machine Learning and get better results.Methods: Various methods and combinations have been carried out in previous research, this research tries to combine Fuzzy and Machine Learning methods, namely Logarithmic Fuzzy Preference Programming (LFPP) and Least Squares Support Vector Machines Machine (LSSVM). LFPP is used to recalculate the cost driver weights and generate Effort Adjustment Point (EAP). The EAP and Lines of Code values are then entered as input for LSSVM. The output results are then measured using the Mean Magnitude of Relative Error (MMRE) and Root-Mean-Square Error (RMSE). In this study, COCOMO and NASA datasets were used.Result: The results obtained are MMRE of 0.015019 and RMSE of 1.703092 on the COCOMO dataset, while on the NASA dataset the results of MMRE are 0.007324 and RMSE are 6.037986. Then 100% of the prediction results meet the 1% range of actual effort on the COCOMO dataset, while on the NASA dataset, the results show that 89,475 meet the 1% range of actual effort and 100% meet the 5% range of actual effort. The results of this study also show a better level of accuracy than using the COCOMO Intermediate method.Novelty: This study uses a combination of LFPP and LSSVM, which is an improvement from previous studies that used a combination of FAHP and LSSVM. The method used is also different where LFPP produces better output than FAHP and all data in the dataset is used for training and testing, whereas in previous research it only used a small part of the data.]]>
<![CDATA[Data Science untuk Karang Taruna : Menggali Peluang Bisnis dan Pengembangan Karir melalui Eksplorasi Data ]]>
<![CDATA[Gata, Windu]]>;
<![CDATA[Hermaliani, Eni Heni]]>;
<![CDATA[Manik, Lindung P.]]>;
<![CDATA[Ernawan, Ferda]]>
<![CDATA[ Jurnal Pengabdian Masyarakat Bangsa Vol. 2 No. 5 (2024): Juli ]]>
Publisher : <![CDATA[Amirul Bangun Bangsa ]]>
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DOI: 10.59837/jpmba.v2i5.1046
<![CDATA[Di era revolusi 4.0 ini, data science semakin populer di berbagai sektor industri, berfungsi sebagai alat penting untuk mengonversi data menjadi informasi berharga. Karir sebagai data scientist dan data analyst adalah dua profesi yang sangat potensial di masa depan, sehingga masyarakat harus terus mengupgrade diri agar terus berkembang mengikuti pertumbuhan ilmu pengetahuan saat ini. Bagi Karang Taruna, diselenggarakannya kegiatan ini dapat memberikan wawasan keilmuan terkait data science dan manfaatnya sebagai peluang bisnis dan pengembangan karir anggotanya. Adapun metode pelaksanaan pelatihan dan kronologis kegiatan abdimas dimulai dengan tahap persiapan yaitu menggali permasalahan yang dihadapi lanjut mengajukan izin penyelenggaraan. Tahap selanjutnya dari kegiatan ini dimulai dengan pembukaan, diikuti oleh presentasi materi, diskusi interaktif, dan sesi tanya jawab. Setelah itu, proses monitoring dan evaluasi dilakukan melalui penyebaran kuesioner untuk mendapatkan umpan balik. Kegiatan ditutup dengan acara penutupan resmi. Program pengabdian ini berhasil memperluas wawasan peserta mengenai teknologi modern dan menekankan pentingnya ilmu data di era digital untuk mengidentifikasi peluang bisnis dan mengembangkan karir. Kompetensi dasar yang diperoleh dapat terus diasah melalui tools-tools ilmu data lainnya dimasa depan.]]>
<![CDATA[Prediksi Cacat Perangkat Lunak Kelas Tidak Seimbang Menggunakan Resample J48 Dan J48 Consolidated ]]>
<![CDATA[Nurjabar, Ilham]]>;
<![CDATA[Manik, Lindung Parningotan]]>
<![CDATA[ INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 4 (2024): INTECOMS: Journal of Information Technology and Computer Science ]]>
Publisher : <![CDATA[Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE) ]]>
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DOI: 10.31539/intecoms.v7i4.10489
<![CDATA[Prediksi cacat perangkat lunak merupakan aspek penting dalam jaminan kualitas perangkat lunak, dengan tujuan mengidentifikasi dan mengatasi potensi cacat sebelum mereka muncul dalam lingkungan produksi. Penelitian ini menyajikan pendekatan inovatif untuk mengatasi masalah distribusi kelas yang tidak seimbang dalam prediksi cacat perangkat lunak menggunakan teknik resampling dan algoritma J48 dan J48 Consolidated. Selain itu, penelitian ini memperkenalkan varian baru dari J48, yang disebut sebagai J48 Consolidated, yang menggabungkan beberapa pohon keputusan menjadi satu model ensemble tunggal untuk meningkatkan kinerja prediksi. Model J48 Consolidated dibandingkan dengan algoritma J48 tradisional dalam konteks prediksi cacat perangkat lunak dengan distribusi kelas yang tidak seimbang. Dataset yang yang digunakan pada penelitian ini menggunakan dataset PROMISE repository. Hasil penelitian menunjukan bahwa integrase Algoritma RUS + J48 Consolidated layak digunakan untuk memprediksi cacat software dengan rata-rata akurasi 78% dengan nilai AUC 0.783. Penelitian ini menguji kinerja J48 dengan pendekata ROS dan RUS menggunakan Algoritma J48 dan J48 Consolidated. Hasil penelitian menunjukan model RUS+J48 Consolidated lebih baik dari model RUS+J48 dengan nilai rata-rata akurasi 78% dan 77% serta nilai AUC 0.783 dan 0.766.]]>
<![CDATA[Comparison of Oversampling Techniques on Minority Data Using Imbalance Software Defect Prediction Dataset ]]>
<![CDATA[Hidayat, Deni]]>;
<![CDATA[Manik, Lindung Parningotan]]>
<![CDATA[ Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024 ]]>
Publisher : <![CDATA[Politeknik Negeri Batam ]]>
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DOI: 10.30871/jaic.v8i2.8605
<![CDATA[Software Defect Prediction Dataset as a component of the Software Defect Prediction model has a very vital role. However, NASA Software Defect Prediction has a problem with imbalance in minority data. This study compares the performance of oversampling techniques in overcoming this. A total of 90 oversampling techniques in the form of SMOTE and its variants were used. The results of this study indicate that there is no oversampling technique that is able to overcome this. The original dataset without oversampling shows good performance at the level of accuracy and f1-score but has low performance on auc-score and g-score. Several oversampling techniques show increased performance on auc-score and g-score, unfortunately at the same time showing a decrease in performance on accuracy and f1-score.]]>
