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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Krea-TIF: Jurnal Teknik Informatika Jurnal Mantik JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Responsif : Riset Sains dan Informatika Jurnal Pengabdian Masyarakat Inovatif Jurnal Pengabdian Masyarakat Inovatif
Gibtha Fitri Laxmi
Universitas Ibn Khaldun Bogor
Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences Other

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The Formation of Optimal Portfolio of Mutual Shares Funds using Multi-Objective Genetic Algorithm Yandra Arkeman; Akhmad Yusuf; Mushthofa Mushthofa; Gibtha FitriLaxmi; Kudang Boro Seminar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 3: September 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i3.1148

Abstract

 Investments in financial assets have become a trend in the globalization era, especially the investment in mutual fund shares. Investors who want to invest in stock mutual funds can set up an investment portfolio in order to generate a minimal risk and maximum return. In this study the authors used the Multi-Objective Genetic Algorithm Non-dominated Sorting II (MOGA NSGA-II) technique with the Markowitz portfolio principle to find the best portfolio from several mutual funds. The data used are 10 company stock mutual funds with a period of 12 months, 24 months and 36 months. The genetic algorithm parameters used are crossover probability of 0.65, mutation probability of 0.05, Generation 400 and a population numbering 20 individuals. The study produced a combination of the best portfolios for the period of 24 months with a computing time of 63,289 seconds.
Region of interest and color moment method for freshwater fish identification Gibtha Fitri Laxmi; Fitrah Satrya Fajar Kusumah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.11749

Abstract

One of the important features in content based image retrieval is color feature. The color feature is the most widely used visual features. Extracting feature image depends on the problem to identify the region or object of interest that is complex in content. This paper presents a methodology to recognize certain freshwater images using region of interest and color feature. In this work, we have considered 7 varieties of freshwater fish, Gourami, Mas/Common carper, Mas Orange, Mas Kancra, Mujair/Java Tilapia, Nila/Nile Tilapia, and Patin. Each variety consists of 20 images. We deployed Color Moment Feature after Region of Interest process to extract the feature. Euclid is used for recognition. Considering only a feature, the classification accuracy of 89% is obtained using color moment. The research technique shows promise for eventually being able to do so, and for the future will help to get important information from the image.
Implementation of KIA Indicator Information System Web Based in Gunung Sindur Health Center, Bogor: Implementation of KIA Indicator Information System Web Based in Gunung Sindur Health Center, Bogor Jejen Jaenudin; Gibtha Fitri Laxmi; Puspa Eosina; Yuggo Afrianto
Jurnal Mantik Vol. 4 No. 1 (2020): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (939.034 KB)

Abstract

The Gunung Sindur Public health centre in its work area handles several villages, each of which has a number of Posyandu cadres in the village. Posyandu activities in addition to providing information about health, also facilitate the community in obtaining health services for mothers, infants and toddlers. In this activity, each village midwife must make a monthly report that is recorded in the cohort book to produce an evaluation as a reference to follow up the performance development activities, and the PWS-KIA calculation process is still done using conventional counting tools that are reported to the Puskesmas coordinator midwife and after it was reported to the Bogor District Health Office as a monitoring target and target for mothers and children so that in the storage of the MCH reporting archives it appeared to be poorly documented. Therefore, this research analyzes and develops a reporting information system to make it easier for coordinating midwives and village midwives to calculate monitoring indicators and document them. The research method for system development uses the waterfall method which consists of analysis, design, programming, and testing. The results of this study developed a web-based KIA indicator information system in the Gunung Sindur Bogor Puskesmas, input data will be processed automatically and presented quickly and neatly in the form of tables and graphs.
SISTEM PAKAR UNTUK MENDIAGNOSIS HAMA DAN PENYAKIT TANAMAN PISANG DENGAN TEOREMA BAYES Lievia Anjhelina Maharani; Gibtha Fitri Laxmi; Freza Riana
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 8 No. 1 (2021)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (840.872 KB) | DOI: 10.33197/jitter.vol8.iss1.2021.710

Abstract

Tanaman pisang merupakan salah satu tanaman yang banyak dijumpai di daerah-daerah di Indonesia, padahal di Indonesia sendiri masih minim informasi yang diterima masyarakat tentang hama dan penyakit yang dapat menyerang tanaman pisang dan cara penanggulangannya. Terbatasnya ketersediaan tenaga ahli di berbagai tempat atau wilayah diIndonesia causes the community to be late in dealing with pests and diseases of banana plants, therefore this research creates a website-based expert system to diagnose pests and diseases of banana plants using 11 data consisting of 5 pests and 6 diseases, as well as 48 symptom data that will be processed using the Bayes Theorem method to produce a probability value of a pest or disease based on the symptoms that arise in banana plants. This study resulted in an accuracy value of 86.53%. The accuracy value is obtained from the comparison between expert diagnosis and the system using the Bayes theorem based on 52 tested data. This expert system can help the community in diagnosing banana plant pests and diseases.
PENERAPAN METODE CERTAINTY FACTOR PADA SISTEM PAKAR DIAGNOSIS PENYAKIT DAN HAMA TANAMAN MANGGIS Farha Fitrahul Janah; Gibtha Fitri Laxmi; Freza Riana
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 8 No. 1 (2021)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (501.968 KB) | DOI: 10.33197/jitter.vol8.iss1.2021.719

Abstract

Mangosteen is one of the horticultural commodities of Indonesian original tropical fruits and has either high value or prospects for cultivation. One of the obstacles in mangosteen cultivation is the low quality of the fruit caused by pests. In general, one of the efforts to prevent and control the spread of diseases and pests in mangosteen plants is to consult with the expert, however this can be an obstacle due to the limited number of experts in an area. Referring to this problem, in this study, a website-based expert system was created to diagnose diseases and pests in mangosteen plants by applying the Certainty Factor method. There were 5 diseases and 5 pests with 42 symptoms, including 25 symptoms of diseases and 17 symptoms of pests that can be identified by this expert system. The percentage value obtained was 80% for the comparison between expert diagnoses and system diagnoses using the Certainty Factor method. This value was obtained based on the suitability of 50 test data that were randomly generated. This expert system can helps mangosteen farmers and the community in diagnosing diseases and pests in mangosteen plants.
Employing PIPRECIA-S weighting with MABAC: a strategy for identifying organizational leadership elections Setiawansyah, Setiawansyah; Hadad, Sitna Hajar; Aldino, Ahmad Ari; Palupiningsih, Pritasari; Fitri Laxmi, Gibtha; Megawaty, Dyah Ayu
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7713

Abstract

The election of organizational leaders, especially in organizations whose members have diverse backgrounds and interests, can cause various problems. Problems in the selection of school organization leaders include the absence of an objective selection of organizational leadership candidates because they are selected based on comparisons between candidates without considering the criteria in the selection of organizational leadership candidates. Research related to the multi-attributive border approximation area comparison (MABAC) and simplified pivot pairwise relative criteria importance assessment (PIPRECIA-S) methods has never been conducted so far, so it is a reference in conducting this research using the MABAC and PIPRECIA-S methods. This study aims to select the head of the school organization using the MABAC method and PIPRECIA-S weighting can increase the objectivity of the criteria assessment results by relying on calculations from the PIPRECIA-S weighting method. Based on the selection results using the MABAC method and PIPRECIA-S weighting, candidate 1 was recommended as the leader of the school organization because it achieved rank 1 with a total score of 0.293. The contribution of this research is to help in the selection of the head of the organization using the PIPRECIA-S and MABAC methods as a decision-making solution.
SISTEM PAKAR UNTUK MENDIAGNOSIS PENYAKIT TANAMAN DURIAN MENGGUNAKAN CERTAINTY FACTOR DAN NAIVE BAYES CLASSIFIER Aljauhari, Muhyiddien Rabbani; Laxmi, Gibtha Fitri; Santoso, Panca Jarot
Jurnal Responsif : Riset Sains dan Informatika Vol 6 No 2 (2024): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v6i2.1487

Abstract

Budidaya durian menghadapi permasalahan serangan penyakit yang dapat merugikan petani secara ekonomi. Salah satu solusi yang dilakukan yaitu berkonsultasi dengan pakar penyakit tanaman durian, tetapi di setiap waktu dan daerah tidak selalu ada seorang pakar. Maka, dibutuhkan sistem yang mampu bekerja seperti pekerjaan pakar dalam mendiagnosis penyakit dari gejala-gejala yang ditemukan. Penelitian ini bertujuan untuk membangun sistem pakar untuk mendiagnosis penyakit tanaman durian dengan perhitungan metode certainty factor dan naive bayes classifier. Langkah-langkah yang dilakukan pada penelitian ini dimulai dengan pengumpulan data, analisis kebutuhan, rancangan sistem, implementasi, kemudian melakukan pengujian sistem dan evaluasi. Dari hasil 30 data uji simulasi yang dibuat secara acak, menggambarkan akurasi sistem dengan metode certainty factor sebesar 90% dan akurasi sistem dengan metode naive bayes classifier sebesar 93%. Bisa disimpulkan dari 30 uji coba data dengan metode naive bayes classifier memiliki akurasi yang lebih tinggi dibandingkan dengan metode certainty factor.
PENERAPAN K-MEANS++ UNTUK PENGELOMPOKAN MAHASISWA BERPOTENSI DROP OUT: STUDI KASUS: UNIVERSITAS IBN KHALDUN BOGOR Putra Nugraha, Raka; Fitri Laxmi, Gibtha; Riana, Freza
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 3 (2024): JATI Vol. 8 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i3.9738

Abstract

Drop out (DO) adalah fenomena penghentian atau pemutusan hubungan studi mahasiswa pada suatu perguruan tinggi yang disebabkan oleh beberapa faktor yang ditentukan oleh universitas tersebut. Fenomena ini merupakan masalah serius karena dapat berdampak pada kualitas perguruan tinggi. Penelitian ini bertujuan untuk mengidentifikasi dan mengelompokkan mahasiswa yang berpotensi drop out (DO) di Universitas Ibn Khaldun Bogor menggunakan algoritma K-Means++. Data yang digunakan merupakan data akademik dari 842 mahasiswa Teknik Informatika angkatan 2016 – 2022, dengan 84 diantaranya berstatus berpotensi drop out. Metode yang digunakan dalam penelitian ini adalah K-Means++ clustering, yang merupakan pengembangan dari algoritma K-Means clustering. K-Means++ clustering digunakan untuk mengatasi kekurangan k-means dalam hal efisiensi waktu dengan menentukan nilai awal centroid secara lebih cerdas, sehingga dapat mengurangi waktu pemrosesan. Hasil penelitian menunjukkan tiga cluster berpotensi DO: (T) tinggi, (S) sedang, dan (R) rendah, dengan mempertimbangkan faktor seperti IPK, jumlah SKS, aktivitas mahasiswa, penghasilan orang tua, dan jalur biaya. Hasil menunjukan faktor berpengaruh ialah IPK, Jumlah Aktif, dan SKS, sedangkan faktor yang tidak berpengaruh ialah Jalur Biaya dan Penghasilan Orang Tua
MODEL DETEKSI JALAN UNTUK SMART GLASSES MENGGUNAKAN ALGORITMA YOLO Firly Djulyansyah, Moechammad; Fitri Laxmi, Gibtha; Agustian H, Sahid
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 4 (2024): JATI Vol. 8 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i4.10620

Abstract

Kemajuan pengetahuan dan teknologi, khususnya di bidang kecerdasan buatan dan pembelajaran mesin, telah mempermudah penyelesaian masalah dan meningkatkan efisiensi kerja manusia. Dalam konteks ini, model Smart Glasses adalah model visi komputer berbasis kecerdasan buatan yang menggunakan algoritma YOLO (You Only Look Once) untuk mendeteksi dan mengidentifikasi kondisi jalan berlubang, jalan menanjak, jalan menurun, gundukan, dan genangan air secara real time. penelitian ini memberikan solusi inovatif untuk mendukung dan membantu penyandang tunanetra dalam melakukan aktivitas sehari-hari, khususnya di daerah yang memiliki kondisi jalan buruk sehingga dapat membahayakan keselamatan mereka. Teknologi deep learning dan pengolahan citra digital digunakan dalam penelitian ini untuk membuat model deteksi jalan untuk Smart Glasses berhasil mencapai tingkat akurasi sebesar 82,8%, membuktikan bahwa model dapat mendeteksi jalan dengan cukup baik. Model dapat dijadikan sebagai alat bantu yang potensial dalam meningkatkan identifikasi kondisi jalan dan keamanan bagi penyandang tunanetra.
Perbandingan Metode Long Short-Term Memory dan Double Random Forest dalam Prediksi Harga Penutupan Saham ZIKRI, MUHAMMAD; Riana, Freza; Laxmi, Gibtha Fitri
Krea-TIF: Jurnal Teknik Informatika Vol 12 No 1 (2024): Krea-TIF 2024
Publisher : Fakultas Teknik dan Sains, Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/krea-tif.v12i1.17468

Abstract

Long Short-Term Memory (LSTM) merupakan bagian dari Recurrent Neural Network (RNN) yang memiliki keunggulan dalam memproses data sekuensial dan mengenali pola serta ketergantungan dalam data berurutan, sementara Double Random Forest (DRF) adalah metode ensemble yang mampu menangkap pola yang kompleks dengan memanfaatkan pohon keputusan dari data pelatihan secara keseluruhan. Kedua metode ini dapat digunakan untuk melakukan forecasting, termasuk dalam kasus prediksi harga saham. Penelitian ini bertujuan untuk membandingkan metode LSTM dan DRF dalam konteks prediksi harga penutupan saham pada PT. Indofood Sukses Makmur, Tbk, menggunakan dataset berukuran 1.253 data yang akan dibagi menjadi 80% data latih dan 20% data uji. Eksperimen ini mencakup tiga skenario, yaitu penggunaan semua fitur yang tersedia, penggunaan fitur dengan korelasi linier positif tinggi, dan penggunaan fitur dengan korelasi linier rendah. Error metrics yang digunakan pada penelitian ini adalah RMSE, MAE, dan MAPE. Dari hasil penelitian prediksi harga penutupan PT. Indofood Sukses Makmur, Tbk diperoleh bahwa LSTM memiliki hasil yang lebih baik dibandingkan DRF dengan nilai RMSE (80.99), MAE (61.08), dan MAPE (0.94). Hasil dari riset ini diharapkan dapat memberikan kontribusi nyata dalam pengembangan sistem prediksi harga saham berbasis machine learning.
Co-Authors Abidin, Riswan Agustian H, Sahid Ahmad Ari Aldino Aljauhari, Muhyiddien Rabbani Anbi, Moningka Naca Apid Robini Eka Prawira Br. Ginting, Novita Budi Susetyo Catur Hermanto Darmawan, Supri Zaki Dewi Primasari Erwin Hermawan Farha Fitrahul Janah Fety Fatimah Firly Djulyansyah, Moechammad Fitrah Satrya Fajar Kusumah Freza Riana Hadjimartsu, Sahid Agustian Hendriawan Nur, Wawan Iksal Yanuarsyah Jejen Jaenudin Kamali, Alviyan Badro Kudang Boro Seminar Lievia Anjhelina Maharani Megawaty, Dyah Ayu Muhammad Zikri Novita Br Ginting Nurul Kamilah Palupiningsih, Pritasari Panca Jarot Santoso Puspa Eosina Putra Nugraha, Raka RR. Ella Evrita Hestiandari Saepulloh, Irwan Sahid Agustian Hudjimartsu Setiawan, Foni Agus Setiawansyah Setiawansyah Sitna Hajar Hadad Wulandari, Berlina Yandra Arkeman Yuggo Afrianto