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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Jurnal Mahasiswa Sistem Informasi (JMSI) Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Ilmiah Sistem Informasi
Susanto, Lilik Joko
Universitas Muhammadiyah Metro
Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science Other

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SISTEM PENDUKUNG KEPUTUSAN PENENTUAN JURUSAN DENGAN METODE ANALYTHICAL HIERARCHY PROCESS ( STUDI KASUS : SMK GANESHA LAMPUNG TIMUR ) Guna Yanti Kemala Sari Siregar Pahu; Pujianto Pujianto; Lilik Joko Susanto
EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Vol 9, No 2 (2019): Desember
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jmsit.v9i2.1313

Abstract

Kelulusan untuk siswa SMP sudah selesai. Saatnya lulusan SMP mencari sekolah untuk melanjutkan pendidikannya ke jenjang yang lebih tinggi. Sebagian besar banyak siswa lulusan SMP melanjutkan ke SMK dengan tujuan agar ketika lulus sudah mempunyai skill dan bisa membuka peluang usaha ataupun bisa langsung bekerja sesuai dengan jurusan mereka ketika melanjutkan pendidikan ke SMK. Hal yang patut disayangkan adalah kurang terarahnya mereka memilih jurusan yang ada di sekolah kejuruan yang dituju. Situasi semacam ini berdampak pada biaya pendidikan yang sudah terlanjur dikeluarkan. Jadi dengan kata lain baik pada orang tua siswa maupun pemerintah yang memberi subsidi sekolah menjadi tidak bermanfaat karena siswa tersebut tidak memiliki kemampuan yang memadai untuk jurusan yang sudah dipilihnya, akibat negatifnya adalah para siswa yang drop out dikarenakan tidak mampu menyelesaikan pendidikan sampai dengan akhir. Berdasarkan informasi yang diperoleh dari team penerimaan siswa kejurusan jumlah jurusan yang ada pada SMK Ganesha terdiri dari 4 Jurusan Akuntansi, Administrasi, Teknik Komputer dan Jaringan Teknik Sepeda Motor, penjurusan ditentukan oleh nilai yang disyaratkan pada masing-masing jurusan. Sistem Pendukung Keputusan pemilihan jurusan dengan Metode Analytical Hierarchy Process. Diharapkan dapat membantu team penerimaan siswa baru dalam menentukan jurusan bagi tiap siswa. Sistem Pendukung Keputusan ini diharapkan membantu sehingga dapat dengan mudah menentukan dimana yang cocok untuk siswa siswi dengan menggunakan nilai yang sudah ada dan dapat diketahui perangkingan dari setiap lokasi, kemudian data akan diolah untuk menentukan jurusan yang tepat untuk masing-masing siswa yang ada.
Penerapan Knowledge Management System Dalam Manajemen Data Kegiatan Hidroponik (Studi Kasus: Komunitas Hidroponik Bandar Lampung) Lilik Joko Susanto; Hendra Kurniawan
Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Vol 1, No 1 (2018): Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data)
Publisher : Institut Informatika dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/simada.v1i1.1108

Abstract

Knowledge is a success key for any aspects in various fields. As a community, Komunitas Hidroponik Bandar Lampung implemented the advancement of information technology in improving the understanding of hydroponic techniques. To maintain the learning process, innovation and renewal is developed and continued. Therefore, it is needed facilities or activities that can facilitate every individual or member to express his/her ideas or opinion on the hydroponic techniques. Website Knowledge Management of Komunitas Hidroponik was designed using Rational Unified Process (RUP) method. The construction of the website using PHP as the scripting language, PHP My Admin as the web server, Atom Text Editor as the interface editor, Adobe Photoshop CS6 as picture editor, Star UML for designing the system diagram design, and MySQL as the database processor. This study resulted in a Website Knowledge Management. The utilization of the website can help the users gaining information, giving venues for discussion, interaction and sharing knowledge on hydroponic techniques. Keywords: Hydroponic; Knowlegde; Knowledge Management System
PERANCANGAN SISTEM INFORMASI PENGOLAHAN DATA PASIEN PADA RUANG POLI ANAK RUMAH SAKIT UMUM MUHAMMADIYAH KOTA METRO Pembri Kurniadi; Lilik Joko Susanto
Jurnal Mahasiswa Sistem Informasi (JMSI) Vol 3 No 2 (2022): Jurnal Mahasiswa Sistem Informasi (JMSI)
Publisher : Program Studi DIII Sistem Informasi - Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jmsi.v3i2.2151

Abstract

Sistem Informasi pengolahan data Pasien pada ruang Poli Anak Rumah Sakit Umum Muhammadiyah Kota Metro yang sedang berjalan masih dilakukan secara manual dan belum maksimal. Dimana terdapat kekurangan-kekurangan yang secara umum masih merupakan kendala bagi kelancaran informasi data Poli Anak, seperti sering terjadi kesalahan dalam penulisan nama, alamat dan sulitnya pengecekan pegawai, dikarenakan jumlah pegawai yang tidak sedikit. Kesalahan-kesalahan ini terjadi karena pegawai merasa jenuh jika harus menulis terus-menerus, kurangnya penggunaan komputer secara maksimal juga keterbatasan kemampuan dalam pengoperasian komputer dan kurangnya keinginan dalam memanfaatkan fasilitas komputer. Metodologi perancangan sistem informasi pengolahan data pasien pada ruang poli anak yang penulis gunakan adalah teknik observasi, wawancara, dan Research perpustakaan. Dalam perancangan ini penulis menggunakan Program Visual Basic versi 6.0. Dengan adanya sistem yang baru ini diharapkan dapat membantu mengatasi ataupun mengurangi permasalahan-permasalahan yang ada dan adanya peningkatan kualitas informasi yang dihasilkan lebih akurat, cepat dan tepat waktu. Sistem pengolahan data pasien pada ruang poli anak yang terkomputerisasi dengan baik akan menghasilkan kinerja yang lebih baik lagi.
Peran Sistem Informasi untuk Mendukung Pengembangan Karir dan Kompetensi (Literature Review) Joko Susanto, Lilik; Pujianto, Pujianto; Asmanto, Budi
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 1 No. 1 (2022): MARET
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v1i1.1954

Abstract

Sistem informasi dapat mendukung berbagai tahapan proses rekrutmen seperti pendaftaran, tes, wawancara, dan sebagainya. Tinjauan pustaka ini bertujuan untuk mengidentifikasi dan mendeskripsikan berbagai metodologi yang dapat digunakan pada pengembangan, persyaratan dan aspek yang dapat mempengaruhi hasil rekrutmen dan dampak yang dihasilkan dari penggunaan sistem untuk mendukung karir dan kompetensi. Penelitian ini menggunakan metodologi PRISMA untuk melakukan studi literatur. Ada empat database penelitian akademik yang digunakan untuk memindai artikel referensi yang relevan, yaitu IEEE Explore, Science Direct, dan JSTOR. Penelitian ini memperoleh 15 literatur dan artikel yang dipilih dikelompokkan berdasarkan database, tahun dan kategori dalam penelitian ini. Hasil dari penelitian ini adalah ringkasan metodologi yang dapat digunakan dalam pengembangan sistem, aspek pendukung pengembangan dan dampak penerapan sistem penggunaan sistem pendukung karir dan kompetensi.
Sistem Pendukung Keputusan Pemilihan Indekost Pemuda Dengan Menggunakan Metode Simple Additive Weighting (Saw) Siregar, Gunayanti Kemalasari; Susanto, Lilik Joko
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 1 No. 2 (2022): OKTOBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v1i2.3173

Abstract

Housing development for youth is currently the main priority because boarding houses are considered ideal and practical places to choose a place to live. The number of enthusiasts who choose boarding houses for housing is due to many considerations, including cost, facilities, and strategic location. Due to these considerations, many young boarding houses choose suitable temporary housing. A Decision Support System (DSS) is a technique that can solve a problem in semi-systematic and non-systematic terms. This decision support system will later be made using the Simple Additive Weighting (SAW) method with the hope of solving the problems experienced by students. The author's research steps are determining criteria and alternatives, collecting data from questionnaires distributed to students, carrying out calculations, and ranking alternatives. From the calculation results, it is obtained that Kost2 is the best alternative.
COMPARATIVE ANALYSIS OF PERFORMANCE OF MACHINE LEARNING FEATURE SELECTION IN EARLY DETECTION OF DIABETES Lilik Joko Susanto; Agus Wantoro
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.473

Abstract

Diabetes is one of the most serious global health problems and continues to increase significantly worldwide. Early detection is essential to reduce complications and improve patient survival rates. Recently, Machine Learning (ML) has shown great potential in supporting early diabetes prediction through data-driven analysis. However, the presence of irrelevant and redundant features may decrease model performance and increase computational complexity. Therefore, this study aims to evaluate the effectiveness of feature selection techniques and ML algorithms for early diabetes detection using the PIMA Indians Diabetes Dataset. The dataset consists of 768 records, 8 features, and two classes. Data preprocessing was conducted to handle missing values and outliers using mean imputation and data cleaning techniques. Three feature selection methods were applied, namely Information Gain (IG), Gain Ratio (GR), and ANOVA, to identify the most relevant features. Furthermore, several ML algorithms, including k-Nearest Neighbor (k-NN), Random Forest, Support Vector Machine (SVM), Naive Bayes, and Neural Network, were evaluated using 10-fold cross-validation. The results showed that feature selection techniques improved classification performance compared to using all features. Glucose, BMI, Age, and Insulin were identified as the most influential features in diabetes prediction. Among all evaluated models, Random Forest combined with ANOVA achieved the best performance with an accuracy of 0.753. In general, the application of feature selection techniques increased model accuracy by up to 3.82%. These findings demonstrate that combining effective feature selection methods with robust ML algorithms can significantly enhance the performance of early diabetes detection systems.
COMPARATIVE STUDY OF CLASSIFICATION MODELS IN PROCESSING STUDENT TEST SCORES DATASETS Rico Pramestiawan; Arry Verdian; Chindu Lintang Bhuana; Lilik Joko Susanto
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.475

Abstract

The development of Machine Learning (ML) has contributed significantly to the field of education, particularly in analyzing student academic data to support data-driven decision-making. Predicting student exam results is important for identifying academic performance patterns, detecting potential failures, and improving learning interventions. However, variations in student characteristics and dataset complexity require the selection of appropriate classification models to achieve optimal prediction performance. This study aims to compare the effectiveness of several ML classification models in predicting student exam results using a student academic dataset. The dataset consists of 306 records, seven attributes, and five grade classes (A, B, C, D, and E), including attendance, quiz scores, midterm examination scores, final examination scores, and assignment scores. Data preprocessing was conducted to handle missing values, duplication, inconsistencies, and outliers. The dataset was split into training and testing data with a ratio of 75:25 and evaluated using 10-fold cross-validation. Several classification models were applied, including k-Nearest Neighbour (kNN), Decision Tree, Naive Bayes, Support Vector Machine (SVM), and Random Forest. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results showed that Random Forest achieved the best performance with an accuracy of 73.9%, precision of 74.0%, recall of 73.9%, and F1-score of 73.9%, followed by Naive Bayes and Decision Tree. Meanwhile, SVM produced the lowest performance among the tested models. The findings indicate that Random Forest is the most effective method for predicting student exam results and has strong potential to support educational decision-making systems.
COMPARATIVE ANALYSIS OF PERFORMANCE OF MACHINE LEARNING FEATURE SELECTION (GINI DECREASE AND RELIEF-F) IN HEART DISEASE DATASET Chindu Lintang Bhuana; Rico Pramestiawan; Lilik Joko Susanto; Arry Verdian
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.477

Abstract

Heart disease remains one of the leading causes of mortality worldwide and presents a major challenge in healthcare systems. Early detection plays an essential role in improving survival rates and minimizing complications through timely intervention. Recent advances in Machine Learning (ML) have provided new opportunities for developing accurate and efficient prediction systems for heart disease detection. However, one of the major challenges in ML-based prediction is identifying the most relevant features to improve classification performance while reducing computational complexity and noise. This study aims to evaluate the effectiveness of two feature selection techniques, namely Gini Decrease (GD) and ReliefF, combined with several ML models, including Support Vector Machine (SVM), Tree, Naïve Bayes, and Random Forest, for heart disease classification. The study employed the UCI Heart Disease Dataset consisting of 303 records and 14 attributes. Data preprocessing included handling missing values using mean imputation, followed by feature selection and classification using 10-fold cross-validation with an 80:20 training-testing ratio. Experimental results showed that ReliefF outperformed GD, achieving the highest average accuracy of 0.796, compared to GD with 0.767 and all features with 0.771. The SVM model achieved the highest accuracy using GD (0.833), while Random Forest demonstrated optimal performance with ReliefF (0.817). Furthermore, the Tree model exhibited the fastest computational time among all evaluated models. These findings indicate that integrating suitable feature selection methods with ML models significantly enhances heart disease classification performance, particularly in improving predictive accuracy and computational efficiency for early medical diagnosis applications.
Co-Authors Agus Wantoro Arry Verdian Asmanto, Budi Chindu Lintang Bhuana Guna Yanti kemala Sari Siregar Pahu Hendra Kurniawan Pembri Kurniadi Pujianto Pujianto Pujianto, Pujianto Rico Pramestiawan Siregar, Gunayanti Kemalasari